New insights into the role of epilithic biological crust in arid rock weathering

This script reproduces all sequence analysis steps and plots included in the paper plus some additional exploratory analyses. The analysis is heavily based on the phyloseq package (McMurdie and Holmes n.d.), but also on many other R packages.

Load data

read.csv("Data/Rock_weathering_new2_otuTab.txt", header = TRUE, row.names = 1, sep = "\t") %>%
  t() %>% 
  as.data.frame() ->
  Rock_weathering_OTUmat

sort_order <- as.numeric(gsub("OTU([0-9]+)", "\\1", colnames(Rock_weathering_OTUmat)))
Rock_weathering_OTUmat <- Rock_weathering_OTUmat[, order(sort_order)]
row.names(Rock_weathering_OTUmat) <- gsub("(.*)Nimrod[0-9]+|Osnat[0-9]+", "\\1", row.names(Rock_weathering_OTUmat))

Metadata <- read.csv("Data/Rock_weathering_metadata_RA.csv", row.names = 1, header = TRUE)
# Order abundance_mat samples according to the metadata
sample_order <- match(row.names(Rock_weathering_OTUmat), row.names(Metadata))
Rock_weathering_OTUmat %<>% arrange(., sample_order)
Metadata$sample_names <- row.names(Metadata)
Metadata$Uni.Source <- fct_collapse(Metadata$Source, Rock = c("Dolomite", "Limestone"))
Metadata$Climate.Source <-
  factor(
    paste(
      Metadata$Climate,
      Metadata$Source
    ),
    levels = c(
      "Arid Limestone",
      "Arid Dust",
      "Arid Loess soil",
      "Hyperarid Dolomite",
      "Hyperarid Dust",
      "Hyperarid Loess soil"
    ),
    labels = c(
      "Arid limestone",
      "Arid dust",
      "Arid loess soil",
      "Hyperarid dolomite",
      "Hyperarid dust",
      "Hyperarid loess soil"
    )
  )

Metadata$Climate.UniSource <-
  factor(
    paste(
      Metadata$Climate,
      Metadata$Uni.Source
    ),
    levels = c(
      "Arid Rock",
      "Arid Dust",
      "Arid Loess soil",
      "Hyperarid Rock",
      "Hyperarid Dust",
      "Hyperarid Loess soil"
    ),
    labels = c(
      "Arid rock",
      "Arid dust",
      "Arid loess soil",
      "Hyperarid rock",
      "Hyperarid dust",
      "Hyperarid loess soil"
    )
  )
# calculate sample size
Metadata$Lib.size = rowSums(Rock_weathering_OTUmat)
row.names(Rock_weathering_OTUmat) <- row.names(Metadata)

# Load taxonomy data
tax.file <- "Data/Rock_weathering_new2_silva.nrv119.taxonomy"
Taxonomy <- read.table(tax.file,  stringsAsFactors = FALSE) # read taxonomy file

# count how many ';' in each cell and add up to 6
for (i in 1:nrow(Taxonomy)) {
  semicolons <- length(gregexpr(";", Taxonomy$V2[i])[[1]])
  if (semicolons < 6) {
    x <- paste0(rep("Unclassified;", 6 - semicolons), collapse = "")
    Taxonomy$V2[i] <- paste0(Taxonomy$V2[i], x, sep = "")
  }
}

do.call( "rbind", strsplit( Taxonomy$V1, ";", fixed = TRUE)) %>% 
  gsub( "size=([0-9]+)", "\\1", .) %>%
  data.frame( ., do.call( "rbind", strsplit( Taxonomy$V2, ";", fixed = TRUE)), stringsAsFactors = F) %>% 
  apply(., 2, function(x) gsub( "\\(.*\\)", "", x)) %>% 
  replace(., . == "unclassified", "Unclassified") -> 
  Taxonomy

colnames( Taxonomy ) <- c( "OTU", "Frequency", "Domain", "Phylum", "Class", "Order", "Family", "Genus" )
# rownames(Taxonomy) <- colnames(Rock_weathering_OTUmat)
rownames(Taxonomy) <- Taxonomy[, 1]

# generate phyloseq object
Rock_dust <- phyloseq(otu_table(Rock_weathering_OTUmat, taxa_are_rows = FALSE),
                        tax_table(Taxonomy[, -c(1, 2)]),
                        sample_data(Metadata)
                        )

# Reorder factors for plotting
sample_data(Rock_dust)$Source %<>% fct_relevel("Limestone", "Dolomite", "Dust", "Loess soil")

Remove samples not for analysis

First let’s explore the prevalence of different taxa in the database.

Phylum Mean prevalence Sum prevalence
Acidobacteria 16.9 1281
Actinobacteria 20.1 5660
Aquificae 14.3 43
Armatimonadetes 15.8 79
Bacteroidetes 16.2 2337
Caldiserica 2.0 2
Candidate_division_BRC1 14.0 28
Candidate_division_OD1 20.2 81
Candidate_division_OP11 9.0 9
Candidate_division_TM7 18.3 440
Chlorobi 15.5 62
Chloroflexi 14.9 2753
Cyanobacteria 19.4 874
Deinococcus-Thermus 19.6 274
Elusimicrobia 11.0 22
Fibrobacteres 16.2 65
Firmicutes 16.2 1164
Fusobacteria 14.0 28
Gemmatimonadetes 15.4 848
Nitrospirae 22.0 44
NPL-UPA2 6.0 6
Planctomycetes 12.7 420
Proteobacteria 19.1 4979
SBYG-2791 9.0 9
SM2F11 20.0 20
Spirochaetae 15.0 15
Synergistetes 11.0 11
Tenericutes 3.0 3
Thaumarchaeota 21.0 21
Verrucomicrobia 13.2 383
WCHB1-60 19.0 57
Order Mean prevalence Sum prevalence
11B-2 9.2 55
Acidimicrobiales 18.2 709
Acidithiobacillales 21.0 21
Actinomycetales 12.0 12
Aeromonadales 21.0 21
AKIW781 11.0 592
AKYG1722 17.4 157
Alteromonadales 14.5 29
Anaerolineales 21.0 63
Aquificales 14.3 43
Ardenticatenales 9.8 98
AT425-EubC11_terrestrial_group 18.2 328
Bacillales 18.3 475
Bacteroidales 10.7 192
BD2-11_terrestrial_group 26.0 26
BD72BR169 14.0 14
Bdellovibrionales 14.5 58
BG.g7 26.0 26
Bifidobacteriales 9.0 18
Brocadiales 15.5 31
Burkholderiales 19.3 559
C0119 12.0 120
Caenarcaniphilales 23.0 23
Caldilineales 17.0 102
Caldisericales 2.0 2
Campylobacterales 14.6 73
Caulobacterales 23.4 257
Chlorobiales 15.5 62
Chloroflexales 7.4 52
Chromatiales 16.8 84
Chthoniobacterales 14.1 226
Clostridiales 15.4 401
Corynebacteriales 16.9 203
Cytophagales 18.6 1113
Dehalococcoidales 8.0 16
Deinococcales 19.6 255
Desulfobacterales 8.4 42
Desulfovibrionales 8.0 8
Desulfurellales 11.0 11
Desulfuromonadales 22.0 22
Elev-16S-976 23.5 47
EMP-G18 3.0 3
Enterobacteriales 27.0 135
Erysipelotrichales 11.0 33
Euzebyales 19.4 252
Fibrobacterales 16.2 65
Flavobacteriales 16.4 164
Frankiales 28.2 367
Fusobacteriales 14.0 28
Gaiellales 18.4 386
Gammaproteobacteria_Incertae_Sedis 8.0 8
Gemmatimonadales 14.4 446
GR-WP33-30 23.0 46
HOC36 8.0 8
JG30-KF-CM45 20.8 604
Kineosporiales 24.2 145
Lactobacillales 14.4 158
Legionellales 9.0 27
Lineage_IIb 9.0 9
Lineage_IV 13.0 13
LNR_A2-18 15.0 15
Methylophilales 11.5 23
Micrococcales 24.3 365
Micromonosporales 16.2 130
Myxococcales 15.4 200
Neisseriales 14.5 58
Nitriliruptorales 21.3 64
Nitrosomonadales 28.0 28
Nitrospirales 22.0 44
NKB5 21.0 21
Obscuribacterales 12.5 50
Oceanospirillales 15.0 15
Opitutales 10.0 30
Order_II 16.0 32
Order_III 16.0 48
Order_IV 26.0 26
Pasteurellales 14.0 14
Phycisphaerales 19.0 19
Planctomycetales 12.3 344
Propionibacteriales 19.6 352
Pseudomonadales 19.6 372
Pseudonocardiales 20.7 352
PYR10d3 22.0 22
Rhizobiales 20.8 955
Rhodobacterales 21.9 219
Rhodocyclales 19.0 57
Rhodospirillales 18.0 450
Rickettsiales 19.2 115
Rubrobacterales 28.5 484
S0134_terrestrial_group 9.6 48
SC-I-84 15.0 15
Selenomonadales 19.0 19
Solirubrobacterales 21.0 989
Sphaerobacterales 14.3 43
Sphingobacteriales 15.3 751
Sphingomonadales 24.0 552
Streptomycetales 23.0 69
Streptosporangiales 22.5 45
Subgroup_10 15.0 30
Subgroup_2 13.0 13
Subgroup_3 16.1 129
Subgroup_4 16.9 523
Subgroup_6 19.2 441
Subgroup_7 18.0 90
SubsectionI 20.7 62
SubsectionII 23.8 285
SubsectionIII 18.5 351
SubsectionIV 19.0 76
Synergistales 11.0 11
Thermales 19.0 19
Thermoanaerobacterales 13.2 53
Thermogemmatisporales 14.5 29
Thermophilales 22.8 91
Thiotrichales 13.0 39
TRA3-20 20.2 101
Unclassified 16.7 2355
Unknown_Order 14.6 73
Vampirovibrionales 12.0 12
Verrucomicrobiales 8.0 24
Xanthomonadales 17.9 233

Based on that we’ll remove all phyla with a prevalence of under 7

## phyloseq-class experiment-level object
## otu_table()   OTU Table:         [ 1259 taxa and 34 samples ]
## sample_data() Sample Data:       [ 34 samples by 13 sample variables ]
## tax_table()   Taxonomy Table:    [ 1259 taxa by 6 taxonomic ranks ]
## phyloseq-class experiment-level object
## otu_table()   OTU Table:         [ 1256 taxa and 34 samples ]
## sample_data() Sample Data:       [ 34 samples by 13 sample variables ]
## tax_table()   Taxonomy Table:    [ 1256 taxa by 6 taxonomic ranks ]

Plot general prevalence features of the phyla

Plot general prevalence features of the top 20 orders

Unsupervised filtering by prevalence

We’ll remove all sequences which appear in less than 10% of the samples

## [1] 3.4
## phyloseq-class experiment-level object
## otu_table()   OTU Table:         [ 1256 taxa and 34 samples ]
## sample_data() Sample Data:       [ 34 samples by 13 sample variables ]
## tax_table()   Taxonomy Table:    [ 1256 taxa by 6 taxonomic ranks ]
## phyloseq-class experiment-level object
## otu_table()   OTU Table:         [ 1249 taxa and 34 samples ]
## sample_data() Sample Data:       [ 34 samples by 13 sample variables ]
## tax_table()   Taxonomy Table:    [ 1249 taxa by 6 taxonomic ranks ]

This removed 7 or 0.557% of the sequences.

Exploring Rock_dust dataset features

First let’s look at the count data distribution

sample_title Lib.size
Arid_Settled Dust_1 1562
Hyperarid_Loess soil_8 6536
Hyperarid_Loess soil_10 3921
Hyperarid_Loess soil_12 4935
Arid_Settled Dust_2 4421
Hyperarid_Settled Dust_1 1001
Hyperarid_Settled Dust_2 16095
Arid_Limestone_1 9765
Arid_Limestone_2 9130
Arid_Limestone_3 11218
Arid_Limestone_4 13838
Arid_Limestone_5 11177
Arid_Limestone_6 10781
Arid_Limestone_7 15417
Arid_Limestone_8 9721
Arid_Limestone_9 20927
Arid_Limestone_10 16812
Arid_Limestone_11 14325
Arid_Limestone_12 5112
Hyperarid_Dolomite_1 62166
Hyperarid_Dolomite_2 73930
Hyperarid_Dolomite_3 123438
Hyperarid_Dolomite_4 74161
Hyperarid_Dolomite_5 98998
Hyperarid_Dolomite_6 97834
Hyperarid_Dolomite_7 160207
Hyperarid_Dolomite_8 78535
Hyperarid_Dolomite_9 47155
Hyperarid_Dolomite_10 52276
Hyperarid_Dolomite_11 63267
Hyperarid_Dolomite_12 53859
Arid_Loess soil_1 61130
Arid_Loess soil_2 62204
Arid_Loess soil_3 55724

The figure and table indicate only a small deviation in the number of reads per samples.

## 
## Call:
## adonis(formula = otu_table(Rock_weathering_filt3) ~ Lib.size,      data = as(sample_data(Rock_weathering_filt3), "data.frame"),      permutations = 9999, method = "bray") 
## 
## Permutation: free
## Number of permutations: 9999
## 
## Terms added sequentially (first to last)
## 
##           Df SumsOfSqs MeanSqs F.Model      R2 Pr(>F)    
## Lib.size   1    2.5245 2.52447  8.7483 0.21469  1e-04 ***
## Residuals 32    9.2341 0.28857         0.78531           
## Total     33   11.7586                 1.00000           
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Account for variation in library read-depth

We’ll use the GMPR method (Chen and Chen 2017)

## Begin GMPR size factor calculation ...
## Completed!
## Please watch for the samples with limited sharing with other samples based on NSS! They may be outliers!
## 
## Call:
## adonis(formula = otu_table(Rock_weathering_filt3_GMPR) ~ Lib.size,      data = as(sample_data(Rock_weathering_filt3_GMPR), "data.frame"),      permutations = 9999, method = "bray") 
## 
## Permutation: free
## Number of permutations: 9999
## 
## Terms added sequentially (first to last)
## 
##           Df SumsOfSqs MeanSqs F.Model      R2 Pr(>F)    
## Lib.size   1    2.5834 2.58337  9.4557 0.22809  1e-04 ***
## Residuals 32    8.7426 0.27321         0.77191           
## Total     33   11.3260                 1.00000           
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Alpha diversity

Calculate and plot alpha diversity mertrics.

# non-parametric richness estimates
rarefaction.mat <- matrix(0, nrow = nsamples(Rock_weathering_filt3), ncol = bootstraps)
rownames(rarefaction.mat) <- sample_names(Rock_weathering_filt3)
rich.ests <- list(S.obs = rarefaction.mat, S.chao1 = rarefaction.mat, se.chao1 = rarefaction.mat,
                   S.ACE = rarefaction.mat, se.ACE = rarefaction.mat)

for (i in seq(bootstraps)) {
  sub.OTUmat <- rrarefy(otu_table(Rock_weathering_filt3), min(rowSums(otu_table(Rock_weathering_filt3))))
  for (j in seq(length(rich.ests))) {
    rich.ests[[j]][, i] <- t(estimateR(sub.OTUmat))[, j]
  }
}

Richness <- data.frame(row.names = row.names(rich.ests[[1]]))
for (i in c(1, seq(2, length(rich.ests), 2))) {
  S <- apply(rich.ests[[i]], 1, mean)
  if (i == 1) { 
    se <- apply(rich.ests[[i]], 1, function(x) (mean(x)/sqrt(length(x))))
    } else se <- apply(rich.ests[[i + 1]], 1, mean)
  Richness <- cbind(Richness, S, se)
}
colnames(Richness) <- c("S.obs", "S.obs.se", "S.chao1", "S.chao1.se", "S.ACE", "S.ACE.se")


saveRDS(Richness, file = "Results/Rock_weathering_Richness.Rds")
write.csv(Richness, file = "Results/Rock_weathering_Richness.csv")

ses <- grep("\\.se", colnames(Richness))
Richness[, ses] %>% 
  gather(key = "est.se") -> se.dat
Richness[, -unique(ses)] %>% 
  gather(key = "est") -> mean.dat

n <- length(unique(mean.dat$est))

# diversity indices
diversity.inds <- list(Shannon = rarefaction.mat, inv.simpson = rarefaction.mat, BP = rarefaction.mat)
for (i in seq(bootstraps)) {
  sub.OTUmat <- rrarefy(otu_table(Rock_weathering_filt3), min(rowSums(otu_table(Rock_weathering_filt3))))
  diversity.inds$Shannon[, i] <- diversityresult(sub.OTUmat, index = 'Shannon', method = 'each site', digits = 3)[, 1]
  diversity.inds$inv.simpson[, i] <- diversityresult(sub.OTUmat, index = 'inverseSimpson', method = 'each site', digits = 3)[, 1]
  diversity.inds$BP[, i] <- diversityresult(sub.OTUmat, index = 'Berger', method = 'each site', digits = 3)[, 1]
}

Diversity <- data.frame(row.names = row.names(diversity.inds[[1]]))
for (i in seq(length(diversity.inds))) {
  S <- apply(diversity.inds[[i]], 1, mean)
  se <- apply(diversity.inds[[i]], 1, function(x) (mean(x)/sqrt(length(x))))
  Diversity <- cbind(Diversity, S, se)
}
colnames(Diversity) <- c("Shannon", "Shannon.se", "Inv.simpson", "Inv.simpson.se", "BP", "BP.se")

ses <- grep("\\.se", colnames(Diversity))
Diversity[, ses] %>% gather(key = "est.se") -> se.dat
Diversity[, -unique(ses)] %>% gather(key = "est") -> mean.dat

saveRDS(Diversity, file = "Results/Rock_weathering_Diversity.Rds")
write.csv(Diversity, file = "Results/Rock_weathering_Diversity.csv")

Test the differences in alpha diversity.

Plot all alpha diversity metrics together

Richness_Diversity_long[Richness_Diversity_long$Metric != "Chao1" &
                          Richness_Diversity_long$Metric != "Inv. Simpson" &
                          Richness_Diversity_long$Metric != "Berger Parker", ] %>% 
  droplevels() ->
  Richness_Diversity_long2plot

p_alpha <- ggplot(Richness_Diversity_long2plot, aes(
  x = Source,
  y = Estimate
)) +
  geom_violin(aes(colour = Climate, fill = Climate), alpha = 1/3) +
  geom_jitter(aes(colour = Climate, fill = Climate), shape = 16, size = 2, width = 0.2, alpha = 2/3) +
  scale_colour_manual(values = pom4, name = "") +
  scale_fill_manual(values = pom4, name = "") +
  theme_cowplot(font_size = 11, font_family = f_name) +
  # geom_errorbar(alpha = 1 / 2, width = 0.3) +
  xlab("") +
  ylab("") +
  theme(axis.text.x = element_text(
    angle = 45,
    vjust = 0.9,
    hjust = 0.9
  )) +
  facet_grid(Metric ~ Climate, shrink = FALSE, scale = "free") +
  background_grid(major = "y",
                  minor = "none") +
  theme(panel.spacing = unit(2, "lines"))

dat_text <- data.frame(
  label = as.character(fct_c(ph_Sobs$groups$groups, ph_ACE$groups$groups, ph_Shannon$groups$groups)),
  Metric   = rep(levels(Richness_Diversity_long2plot$Metric), each = 6),
  Climate = str_split(rownames(ph_Sobs$groups), ":", simplify = TRUE)[, 1], 
  x = c("Loess soil", "Loess soil", "Limestone", "Dust", "Dolomite", "Dust"),
  # x     = as.factor(levels(Richness_Diversity_long2plot$Climate.Source)),
  y = rep(c(460, 850, 6.5), each = 6)
  # y = rep(c(40, 140, 0.5), each = 6)
)


p_alpha <- p_alpha + geom_text(
  data    = dat_text,
  mapping = aes(x = x, y = y, label = label),
  nudge_x = -0.2,
  nudge_y = -0.1
)

print(p_alpha)

Metric Climate.Source mean_PL sd_PL n_PL SE_PL
S obs. Arid limestone 181.62 51.168 12 14.771
S obs. Arid dust 169.25 109.015 2 77.085
S obs. Arid loess soil 416.02 8.277 3 4.779
S obs. Hyperarid dolomite 128.76 31.256 12 9.023
S obs. Hyperarid dust 107.26 88.726 2 62.739
S obs. Hyperarid loess soil 220.41 54.399 3 31.407
ACE Arid limestone 353.78 68.810 12 19.864
ACE Arid dust 334.65 143.601 2 101.541
ACE Arid loess soil 746.50 20.794 3 12.005
ACE Hyperarid dolomite 314.74 100.430 12 28.992
ACE Hyperarid dust 311.05 182.765 2 129.234
ACE Hyperarid loess soil 466.26 42.579 3 24.583
Shannon Arid limestone 3.78 1.094 12 0.316
Shannon Arid dust 3.00 1.563 2 1.106
Shannon Arid loess soil 5.59 0.032 3 0.018
Shannon Hyperarid dolomite 3.33 0.268 12 0.077
Shannon Hyperarid dust 1.48 0.889 2 0.629
Shannon Hyperarid loess soil 3.78 0.851 3 0.492

Beta diversity

Calculate and plot beta diversity mertrics.

1. Possible changes in biofilm arid vs hyper arid

Is there a difference between the two sites. However, since we know that that samples are of different nature we’ll have to control for rock type, source and location:

## 
## Call:
## adonis(formula = otu_table(Rock_weathering_filt3_GMPR) ~ Climate *      Source * Location, data = as(sample_data(Rock_weathering_filt3_GMPR),      "data.frame"), permutations = 9999, method = "horn") 
## 
## Permutation: free
## Number of permutations: 9999
## 
## Terms added sequentially (first to last)
## 
##                  Df SumsOfSqs MeanSqs F.Model      R2 Pr(>F)    
## Climate           1    2.5299 2.52988 21.3820 0.21632 0.0001 ***
## Source            3    4.7060 1.56865 13.2579 0.40238 0.0001 ***
## Location          1    0.4870 0.48696  4.1157 0.04164 0.0042 ** 
## Climate:Source    1    0.4397 0.43972  3.7164 0.03760 0.0039 ** 
## Climate:Location  1    0.4605 0.46052  3.8922 0.03938 0.0042 ** 
## Source:Location   1    0.1142 0.11421  0.9653 0.00977 0.4902    
## Residuals        25    2.9580 0.11832         0.25292           
## Total            33   11.6952                 1.00000           
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Call:
## adonis(formula = otu_table(Rock_weathering_filt3_GMPR_Arid) ~      Source * Location, data = as(sample_data(Rock_weathering_filt3_GMPR_Arid),      "data.frame"), permutations = 9999, method = "horn") 
## 
## Permutation: free
## Number of permutations: 9999
## 
## Terms added sequentially (first to last)
## 
##           Df SumsOfSqs MeanSqs F.Model      R2 Pr(>F)    
## Source     2    2.3058 1.15288  7.3438 0.47881 0.0001 ***
## Location   1    0.4691 0.46905  2.9878 0.09740 0.0144 *  
## Residuals 13    2.0408 0.15699         0.42379           
## Total     16    4.8156                 1.00000           
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Call:
## adonis(formula = otu_table(Rock_weathering_filt3_GMPR_Hyperarid) ~      Source * Location, data = as(sample_data(Rock_weathering_filt3_GMPR_Hyperarid),      "data.frame"), permutations = 9999, method = "horn") 
## 
## Permutation: free
## Number of permutations: 9999
## 
## Terms added sequentially (first to last)
## 
##                 Df SumsOfSqs MeanSqs F.Model      R2 Pr(>F)    
## Source           2    2.8454 1.42270 18.6150 0.65416 0.0001 ***
## Location         1    0.4729 0.47295  6.1882 0.10873 0.0112 *  
## Source:Location  1    0.1142 0.11421  1.4944 0.02626 0.2422    
## Residuals       12    0.9171 0.07643         0.21085           
## Total           16    4.3497                 1.00000           
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

According to this model we see that indeed there’s an effect of site on the community (p = 0.001), and that effect accounts for about 17% of the variance. Also, considering that Location is only borderline significant and explains very little of the data, we could probably take it out of the model to make a minimal adequate model.

## 
## Call:
## adonis(formula = otu_table(Rock_weathering_filt3_GMPR) ~ Climate *      Source, data = as(sample_data(Rock_weathering_filt3_GMPR),      "data.frame"), permutations = 9999, method = "horn") 
## 
## Permutation: free
## Number of permutations: 9999
## 
## Terms added sequentially (first to last)
## 
##                Df SumsOfSqs MeanSqs F.Model    R2 Pr(>F)    
## Climate         1      2.53   2.530   17.65 0.216 0.0001 ***
## Source          3      4.71   1.569   10.94 0.402 0.0001 ***
## Climate:Source  1      0.45   0.445    3.11 0.038 0.0086 ** 
## Residuals      28      4.01   0.143         0.343           
## Total          33     11.70                 1.000           
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Call:
## adonis(formula = otu_table(Rock_weathering_filt3_GMPR_Arid) ~      Source, data = as(sample_data(Rock_weathering_filt3_GMPR_Arid),      "data.frame"), permutations = 9999, method = "horn") 
## 
## Permutation: free
## Number of permutations: 9999
## 
## Terms added sequentially (first to last)
## 
##           Df SumsOfSqs MeanSqs F.Model    R2 Pr(>F)    
## Source     2      2.31   1.153    6.43 0.479  2e-04 ***
## Residuals 14      2.51   0.179         0.521           
## Total     16      4.82                 1.000           
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Call:
## adonis(formula = otu_table(Rock_weathering_filt3_GMPR_Hyperarid) ~      Source, data = as(sample_data(Rock_weathering_filt3_GMPR_Hyperarid),      "data.frame"), permutations = 9999, method = "horn") 
## 
## Permutation: free
## Number of permutations: 9999
## 
## Terms added sequentially (first to last)
## 
##           Df SumsOfSqs MeanSqs F.Model    R2 Pr(>F)    
## Source     2      2.85   1.423    13.2 0.654  1e-04 ***
## Residuals 14      1.50   0.107         0.346           
## Total     16      4.35                 1.000           
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Final model

## 
## Call:
## adonis(formula = otu_table(Rock_weathering_filt3_GMPR) ~ Climate *      Source, data = as(sample_data(Rock_weathering_filt3_GMPR),      "data.frame"), permutations = 9999, method = "horn") 
## 
## Permutation: free
## Number of permutations: 9999
## 
## Terms added sequentially (first to last)
## 
##                Df SumsOfSqs MeanSqs F.Model    R2 Pr(>F)    
## Climate         1      2.53   2.530   17.65 0.216 0.0001 ***
## Source          3      4.71   1.569   10.94 0.402 0.0001 ***
## Climate:Source  1      0.45   0.445    3.11 0.038 0.0086 ** 
## Residuals      28      4.01   0.143         0.343           
## Total          33     11.70                 1.000           
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##                                         pairs total.DF F.Model    R2 p.value p.adjusted
## 1           Arid dust vs Hyperarid loess soil        4    4.80 0.616  0.1000     0.1154
## 2                 Arid dust vs Hyperarid dust        3    2.99 0.599  0.3333     0.3333
## 3                 Arid dust vs Arid limestone       13    4.61 0.277  0.0094     0.0223
## 4             Arid dust vs Hyperarid dolomite       13    6.59 0.355  0.0119     0.0223
## 5                Arid dust vs Arid loess soil        4    5.36 0.641  0.1000     0.1154
## 6      Hyperarid loess soil vs Hyperarid dust        4   10.87 0.784  0.2000     0.2143
## 7      Hyperarid loess soil vs Arid limestone       14    5.59 0.301  0.0078     0.0223
## 8  Hyperarid loess soil vs Hyperarid dolomite       14   16.02 0.552  0.0028     0.0105
## 9     Hyperarid loess soil vs Arid loess soil        5   95.51 0.960  0.1000     0.1154
## 10           Hyperarid dust vs Arid limestone       13    5.34 0.308  0.0327     0.0545
## 11       Hyperarid dust vs Hyperarid dolomite       13   11.62 0.492  0.0118     0.0223
## 12          Hyperarid dust vs Arid loess soil        4  205.85 0.986  0.1000     0.1154
## 13       Arid limestone vs Hyperarid dolomite       23   21.90 0.499  0.0001     0.0015
## 14          Arid limestone vs Arid loess soil       14    9.11 0.412  0.0018     0.0090
## 15      Hyperarid dolomite vs Arid loess soil       14   16.84 0.564  0.0017     0.0090
##    sig
## 1     
## 2     
## 3    .
## 4    .
## 5     
## 6     
## 7    .
## 8    .
## 9     
## 10    
## 11   .
## 12    
## 13   *
## 14   *
## 15   *
## cumulative contributions of most influential species:
## 
## $`Arid dust_Hyperarid loess soil`
##    OTU6   OTU65  OTU838   OTU90  OTU596   OTU11  OTU187   OTU93  OTU746  OTU167  OTU711 
##   0.222   0.368   0.406   0.430   0.453   0.469   0.484   0.496   0.509   0.521   0.532 
##  OTU121  OTU144   OTU99  OTU194  OTU105  OTU356  OTU340  OTU115  OTU640   OTU55  OTU298 
##   0.543   0.553   0.562   0.571   0.579   0.586   0.593   0.599   0.606   0.612   0.619 
##  OTU715   OTU88  OTU197   OTU48  OTU221  OTU301 OTU1047  OTU322   OTU16  OTU586   OTU46 
##   0.625   0.631   0.638   0.643   0.649   0.654   0.659   0.665   0.669   0.674   0.678 
##  OTU172  OTU386   OTU67  OTU333  OTU854  OTU883 
##   0.683   0.687   0.691   0.695   0.698   0.702 
## 
## $`Arid dust_Hyperarid dust`
##   OTU6  OTU65 OTU838  OTU55 
##  0.492  0.652  0.697  0.730 
## 
## $`Arid dust_Arid limestone`
##    OTU6   OTU65   OTU20   OTU40  OTU838  OTU225  OTU422  OTU596   OTU73   OTU46   OTU11 
##   0.112   0.222   0.292   0.357   0.385   0.402   0.419   0.435   0.451   0.464   0.477 
##  OTU388  OTU119   OTU29   OTU18   OTU34  OTU746  OTU711   OTU16  OTU235  OTU854  OTU174 
##   0.488   0.499   0.509   0.519   0.529   0.538   0.547   0.556   0.564   0.573   0.581 
##  OTU299  OTU194   OTU68  OTU545   OTU37  OTU925   OTU41  OTU115  OTU140  OTU356   OTU60 
##   0.589   0.596   0.603   0.609   0.616   0.623   0.629   0.635   0.640   0.646   0.651 
##  OTU137  OTU197  OTU166  OTU605   OTU69   OTU55 OTU1089   OTU27  OTU218   OTU48   OTU45 
##   0.656   0.661   0.666   0.671   0.676   0.681   0.685   0.690   0.694   0.698   0.702 
## 
## $`Arid dust_Hyperarid dolomite`
##   OTU1   OTU2   OTU3   OTU7   OTU5  OTU65   OTU9  OTU18   OTU8  OTU17  OTU41  OTU16 
##  0.153  0.236  0.317  0.382  0.415  0.445  0.470  0.495  0.517  0.538  0.559  0.576 
##  OTU11 OTU936  OTU20  OTU28  OTU12  OTU10   OTU6  OTU15  OTU33  OTU14 
##  0.593  0.609  0.624  0.638  0.653  0.666  0.677  0.687  0.696  0.706 
## 
## $`Arid dust_Arid loess soil`
##   OTU65   OTU25   OTU88   OTU62   OTU11  OTU838   OTU68  OTU596  OTU177   OTU78  OTU133 
##  0.0760  0.0986  0.1205  0.1406  0.1597  0.1783  0.1907  0.2015  0.2119  0.2224  0.2320 
##  OTU144   OTU46  OTU115   OTU67   OTU16  OTU156  OTU388  OTU116  OTU137  OTU197  OTU100 
##  0.2412  0.2501  0.2587  0.2669  0.2742  0.2815  0.2885  0.2950  0.3013  0.3076  0.3139 
##   OTU91  OTU687  OTU746  OTU711   OTU73  OTU412  OTU194  OTU422  OTU160  OTU218  OTU152 
##  0.3198  0.3256  0.3312  0.3367  0.3423  0.3473  0.3521  0.3569  0.3615  0.3661  0.3705 
##   OTU53  OTU131  OTU135  OTU356  OTU125  OTU163   OTU37  OTU556  OTU311  OTU130  OTU214 
##  0.3749  0.3789  0.3827  0.3866  0.3904  0.3940  0.3975  0.4011  0.4046  0.4081  0.4116 
##  OTU227  OTU240  OTU341  OTU233  OTU854  OTU648  OTU101   OTU92   OTU55  OTU716  OTU220 
##  0.4150  0.4184  0.4217  0.4250  0.4283  0.4315  0.4348  0.4380  0.4412  0.4443  0.4474 
##  OTU139  OTU140  OTU278   OTU20  OTU103   OTU60  OTU253  OTU171 OTU1235  OTU107  OTU365 
##  0.4505  0.4536  0.4565  0.4594  0.4623  0.4651  0.4679  0.4706  0.4733  0.4760  0.4787 
##  OTU294  OTU301  OTU647  OTU206  OTU136  OTU231  OTU479 OTU1050 OTU1288   OTU82  OTU248 
##  0.4813  0.4839  0.4864  0.4890  0.4916  0.4941  0.4966  0.4991  0.5015  0.5039  0.5064 
##  OTU360  OTU181  OTU434  OTU945  OTU638 OTU1102  OTU315 OTU1184  OTU457   OTU44  OTU491 
##  0.5087  0.5111  0.5134  0.5157  0.5180  0.5203  0.5226  0.5249  0.5271  0.5294  0.5316 
##  OTU472  OTU938  OTU184  OTU471  OTU182  OTU538  OTU352  OTU169  OTU586  OTU191  OTU190 
##  0.5338  0.5360  0.5382  0.5404  0.5425  0.5446  0.5467  0.5488  0.5509  0.5530  0.5551 
##  OTU751  OTU304  OTU251  OTU164  OTU667   OTU69  OTU619  OTU170  OTU154  OTU992 OTU1013 
##  0.5571  0.5592  0.5612  0.5632  0.5652  0.5672  0.5692  0.5712  0.5731  0.5751  0.5771 
##  OTU707  OTU302  OTU725  OTU430 OTU1116  OTU132  OTU466  OTU364  OTU489  OTU166   OTU33 
##  0.5790  0.5809  0.5828  0.5847  0.5866  0.5885  0.5904  0.5922  0.5941  0.5959  0.5978 
## OTU1218  OTU421 OTU1319  OTU545  OTU309  OTU456  OTU562  OTU635  OTU468  OTU610  OTU347 
##  0.5996  0.6015  0.6033  0.6051  0.6068  0.6086  0.6103  0.6121  0.6138  0.6155  0.6171 
##  OTU833  OTU582   OTU41  OTU333    OTU6   OTU27   OTU86  OTU451  OTU162  OTU561  OTU439 
##  0.6188  0.6205  0.6222  0.6238  0.6255  0.6271  0.6287  0.6302  0.6318  0.6334  0.6349 
##  OTU579  OTU255  OTU653  OTU279  OTU368  OTU783   OTU93  OTU298  OTU323 OTU1024 OTU1005 
##  0.6364  0.6380  0.6395  0.6410  0.6425  0.6440  0.6455  0.6470  0.6484  0.6499  0.6513 
##  OTU389  OTU799  OTU348  OTU543  OTU524  OTU258  OTU953  OTU929  OTU174 OTU1100  OTU999 
##  0.6528  0.6542  0.6556  0.6570  0.6584  0.6598  0.6612  0.6625  0.6639  0.6652  0.6665 
##  OTU705  OTU199 OTU1049 OTU1242  OTU449  OTU303  OTU232  OTU765  OTU499   OTU10 OTU1011 
##  0.6678  0.6691  0.6704  0.6717  0.6730  0.6742  0.6754  0.6767  0.6779  0.6791  0.6803 
##  OTU595  OTU697  OTU548  OTU353  OTU310   OTU28 OTU1170  OTU404  OTU141  OTU277  OTU469 
##  0.6815  0.6827  0.6839  0.6851  0.6863  0.6875  0.6887  0.6898  0.6910  0.6922  0.6933 
##  OTU883  OTU557   OTU95   OTU29 OTU1108  OTU641 
##  0.6944  0.6956  0.6967  0.6978  0.6990  0.7001 
## 
## $`Hyperarid loess soil_Hyperarid dust`
##    OTU6   OTU55   OTU90   OTU11   OTU94  OTU187   OTU93  OTU167  OTU121   OTU99  OTU144 
##   0.455   0.493   0.518   0.536   0.551   0.565   0.578   0.589   0.600   0.610   0.619 
##    OTU9  OTU105  OTU340  OTU298  OTU115  OTU640  OTU715   OTU88  OTU197  OTU221  OTU322 
##   0.627   0.634   0.641   0.648   0.654   0.661   0.667   0.674   0.680   0.685   0.690 
## OTU1047   OTU16  OTU484 
##   0.695   0.700   0.704 
## 
## $`Hyperarid loess soil_Arid limestone`
##    OTU6   OTU20   OTU40   OTU90  OTU225   OTU11   OTU73  OTU422  OTU119   OTU29  OTU187 
##   0.207   0.274   0.338   0.356   0.373   0.389   0.403   0.418   0.429   0.439   0.449 
##   OTU46  OTU388   OTU18  OTU167   OTU34   OTU93  OTU299  OTU235  OTU121  OTU174   OTU99 
##   0.459   0.469   0.478   0.487   0.495   0.504   0.512   0.520   0.527   0.535   0.541 
##  OTU925  OTU854  OTU545  OTU144   OTU37  OTU197  OTU140   OTU68   OTU16  OTU340  OTU298 
##   0.548   0.554   0.560   0.566   0.572   0.578   0.584   0.589   0.594   0.599   0.604 
##  OTU605  OTU640   OTU69  OTU715  OTU105   OTU41  OTU137   OTU88   OTU27 OTU1089   OTU60 
##   0.609   0.614   0.618   0.623   0.628   0.632   0.637   0.641   0.645   0.649   0.653 
##   OTU45  OTU221  OTU166  OTU218  OTU261  OTU163   OTU81  OTU115  OTU322  OTU259 OTU1047 
##   0.658   0.662   0.665   0.669   0.673   0.677   0.680   0.684   0.687   0.690   0.693 
##  OTU172  OTU939   OTU67 
##   0.696   0.699   0.702 
## 
## $`Hyperarid loess soil_Hyperarid dolomite`
##   OTU1   OTU2   OTU3   OTU7   OTU6   OTU5   OTU9  OTU18   OTU8  OTU17  OTU41  OTU16 
##  0.150  0.232  0.312  0.374  0.421  0.454  0.478  0.501  0.523  0.544  0.564  0.580 
## OTU936  OTU11  OTU28  OTU12  OTU20  OTU10  OTU15  OTU14  OTU13  OTU45 
##  0.595  0.610  0.625  0.639  0.653  0.666  0.676  0.686  0.694  0.703 
## 
## $`Hyperarid loess soil_Arid loess soil`
##    OTU6   OTU25   OTU62   OTU88   OTU90   OTU11   OTU68  OTU177   OTU78  OTU133  OTU156 
##   0.117   0.141   0.161   0.180   0.192   0.204   0.216   0.226   0.237   0.247   0.254 
##  OTU187   OTU46  OTU116   OTU67  OTU137  OTU100  OTU687  OTU388  OTU115  OTU121   OTU73 
##   0.262   0.269   0.275   0.281   0.287   0.293   0.299   0.305   0.311   0.316   0.321 
##  OTU412   OTU91  OTU167   OTU93   OTU16   OTU99  OTU160  OTU218  OTU144  OTU131  OTU135 
##   0.327   0.332   0.337   0.342   0.347   0.352   0.357   0.361   0.366   0.370   0.374 
##   OTU53  OTU556  OTU152  OTU130  OTU125  OTU227  OTU340  OTU214  OTU233  OTU640  OTU341 
##   0.378   0.381   0.385   0.389   0.392   0.396   0.399   0.403   0.406   0.410   0.413 
##  OTU311  OTU648  OTU422  OTU716  OTU105  OTU197  OTU163  OTU140   OTU44   OTU37   OTU92 
##   0.416   0.420   0.423   0.426   0.430   0.433   0.436   0.439   0.442   0.445   0.448 
##  OTU240  OTU715  OTU139  OTU253  OTU221  OTU298  OTU101  OTU171 OTU1235  OTU107  OTU365 
##   0.451   0.454   0.457   0.460   0.463   0.466   0.469   0.472   0.474   0.477   0.480 
##  OTU491  OTU103  OTU647  OTU479 OTU1047 OTU1288  OTU278  OTU322  OTU136 OTU1102 OTU1050 
##   0.482   0.485   0.488   0.490   0.493   0.495   0.498   0.500   0.503   0.506   0.508 
##  OTU206  OTU360  OTU220  OTU248  OTU231   OTU48  OTU638  OTU945  OTU315  OTU294  OTU181 
##   0.510   0.513   0.515   0.518   0.520   0.522   0.525   0.527   0.529   0.531   0.534 
##  OTU190  OTU471  OTU172  OTU169  OTU457   OTU82  OTU191 OTU1013  OTU472  OTU182  OTU251 
##   0.536   0.538   0.540   0.542   0.545   0.547   0.549   0.551   0.553   0.555   0.557 
##  OTU619  OTU184  OTU352  OTU938  OTU154  OTU707  OTU304  OTU538  OTU386 OTU1116  OTU582 
##   0.559   0.561   0.564   0.566   0.568   0.570   0.572   0.574   0.576   0.578   0.580 
##  OTU751  OTU434   OTU20 OTU1319 OTU1218  OTU992  OTU309  OTU456  OTU364  OTU302   OTU69 
##   0.582   0.583   0.585   0.587   0.589   0.591   0.593   0.595   0.597   0.598   0.600 
##  OTU468  OTU725   OTU86  OTU466  OTU562 OTU1184  OTU347  OTU430  OTU164   OTU60  OTU451 
##   0.602   0.604   0.605   0.607   0.609   0.611   0.612   0.614   0.616   0.617   0.619 
##  OTU635  OTU489  OTU854  OTU610  OTU132  OTU439  OTU255 OTU1046  OTU421  OTU170  OTU543 
##   0.621   0.622   0.624   0.625   0.627   0.629   0.630   0.632   0.633   0.635   0.636 
##  OTU579 OTU1024   OTU27  OTU323  OTU929  OTU368  OTU953  OTU799  OTU545  OTU258 OTU1282 
##   0.638   0.639   0.641   0.642   0.644   0.645   0.647   0.648   0.650   0.651   0.652 
##  OTU267   OTU28 OTU1100  OTU277  OTU279  OTU667  OTU199  OTU705  OTU999  OTU232   OTU95 
##   0.654   0.655   0.657   0.658   0.659   0.661   0.662   0.663   0.665   0.666   0.667 
##  OTU303  OTU836 OTU1242  OTU653    OTU3  OTU141  OTU697  OTU273  OTU499   OTU29 OTU1011 
##   0.669   0.670   0.671   0.672   0.674   0.675   0.676   0.677   0.679   0.680   0.681 
##  OTU389  OTU162 OTU1108  OTU166  OTU595  OTU524  OTU185  OTU765   OTU41  OTU404  OTU641 
##   0.682   0.683   0.685   0.686   0.687   0.688   0.689   0.691   0.692   0.693   0.694 
##  OTU557 OTU1005 OTU1049   OTU10 OTU1101  OTU449 
##   0.695   0.696   0.698   0.699   0.700   0.701 
## 
## $`Hyperarid dust_Arid limestone`
##   OTU6  OTU20  OTU40  OTU55 OTU225 OTU422  OTU73  OTU11  OTU46  OTU94 OTU388  OTU29 
##  0.383  0.442  0.497  0.524  0.539  0.553  0.566  0.578  0.589  0.600  0.609  0.618 
## OTU119  OTU18  OTU34 OTU235  OTU16 OTU854 OTU174 OTU299  OTU68 OTU545   OTU9  OTU37 
##  0.627  0.636  0.644  0.651  0.659  0.666  0.673  0.680  0.685  0.691  0.697  0.702 
## 
## $`Hyperarid dust_Hyperarid dolomite`
##   OTU6   OTU1   OTU2   OTU3   OTU7   OTU5  OTU18   OTU9   OTU8  OTU17  OTU41  OTU11 
##  0.144  0.281  0.357  0.430  0.487  0.517  0.538  0.559  0.579  0.598  0.617  0.633 
##  OTU16 OTU936  OTU28  OTU20  OTU12 
##  0.648  0.662  0.676  0.689  0.701 
## 
## $`Hyperarid dust_Arid loess soil`
##    OTU6   OTU55   OTU25   OTU88   OTU11   OTU62   OTU68  OTU177   OTU78   OTU94  OTU133 
##   0.272   0.292   0.310   0.327   0.343   0.359   0.369   0.377   0.385   0.393   0.401 
##  OTU144   OTU46  OTU115   OTU67  OTU156   OTU16  OTU388  OTU116  OTU137  OTU197  OTU100 
##   0.408   0.415   0.422   0.428   0.434   0.440   0.445   0.450   0.455   0.460   0.465 
##  OTU687   OTU91   OTU73  OTU412  OTU218    OTU9  OTU422  OTU160   OTU53  OTU152  OTU125 
##   0.470   0.474   0.479   0.482   0.486   0.490   0.494   0.497   0.501   0.504   0.508 
##  OTU135  OTU131  OTU311  OTU556  OTU163  OTU130  OTU484  OTU214  OTU227  OTU240   OTU37 
##   0.511   0.514   0.517   0.520   0.523   0.526   0.528   0.531   0.534   0.536   0.539 
##  OTU341  OTU233  OTU854  OTU101  OTU648  OTU140  OTU716   OTU92  OTU220  OTU139   OTU44 
##   0.542   0.544   0.547   0.549   0.552   0.554   0.557   0.559   0.562   0.564   0.567 
##  OTU491   OTU20  OTU278  OTU103  OTU253   OTU60 OTU1235  OTU206  OTU171  OTU107  OTU365 
##   0.569   0.571   0.574   0.576   0.578   0.580   0.582   0.584   0.587   0.589   0.591 
##  OTU294  OTU647  OTU136  OTU231 OTU1050 OTU1102  OTU479 OTU1288   OTU82  OTU248  OTU434 
##   0.593   0.595   0.597   0.599   0.601   0.603   0.605   0.607   0.609   0.610   0.612 
##  OTU638  OTU360  OTU945  OTU181  OTU457  OTU472  OTU938 OTU1184  OTU184  OTU315   OTU33 
##   0.614   0.616   0.618   0.619   0.621   0.623   0.625   0.626   0.628   0.630   0.632 
##  OTU667  OTU471  OTU190 OTU1013  OTU182  OTU538  OTU352  OTU191   OTU86  OTU169  OTU586 
##   0.633   0.635   0.637   0.638   0.640   0.642   0.643   0.645   0.647   0.648   0.650 
##  OTU304  OTU251  OTU164  OTU619  OTU170  OTU751 OTU1116  OTU707  OTU582  OTU302  OTU154 
##   0.651   0.653   0.655   0.656   0.658   0.659   0.661   0.662   0.664   0.665   0.667 
##   OTU69  OTU466  OTU725  OTU430  OTU132  OTU545  OTU489  OTU166   OTU48  OTU992  OTU421 
##   0.668   0.670   0.671   0.673   0.674   0.676   0.677   0.679   0.680   0.682   0.683 
## OTU1218 OTU1319  OTU456  OTU309  OTU364  OTU635  OTU437  OTU610  OTU562  OTU298  OTU468 
##   0.685   0.686   0.687   0.689   0.690   0.692   0.693   0.694   0.696   0.697   0.698 
##  OTU347  OTU333 
##   0.700   0.701 
## 
## $`Arid limestone_Hyperarid dolomite`
##   OTU1   OTU2   OTU3   OTU6   OTU7   OTU5   OTU9   OTU8  OTU18  OTU17  OTU41  OTU40 
##  0.144  0.224  0.302  0.361  0.419  0.451  0.474  0.496  0.516  0.537  0.554  0.570 
##  OTU11 OTU936  OTU16  OTU28  OTU12  OTU20  OTU10  OTU15  OTU14  OTU13  OTU33 
##  0.585  0.600  0.615  0.629  0.642  0.656  0.669  0.679  0.688  0.696  0.705 
## 
## $`Arid limestone_Arid loess soil`
##    OTU6   OTU40   OTU20   OTU25   OTU88   OTU62   OTU11  OTU225  OTU177   OTU78  OTU133 
##  0.0976  0.1375  0.1768  0.1973  0.2164  0.2334  0.2485  0.2580  0.2674  0.2765  0.2848 
##   OTU73   OTU68  OTU119  OTU144   OTU67  OTU156  OTU422   OTU18   OTU29   OTU34  OTU100 
##  0.2931  0.3001  0.3071  0.3139  0.3207  0.3273  0.3334  0.3394  0.3450  0.3506  0.3560 
##  OTU116   OTU91  OTU687   OTU46  OTU299  OTU388  OTU235  OTU197  OTU115  OTU218  OTU412 
##  0.3613  0.3664  0.3714  0.3762  0.3810  0.3858  0.3906  0.3952  0.3995  0.4039  0.4082 
##  OTU160  OTU174   OTU53  OTU152  OTU925  OTU135  OTU137   OTU37  OTU854  OTU545  OTU163 
##  0.4123  0.4163  0.4202  0.4242  0.4281  0.4317  0.4353  0.4386  0.4420  0.4452  0.4484 
##  OTU125  OTU130  OTU131  OTU556  OTU605  OTU311   OTU69  OTU140  OTU341  OTU214   OTU27 
##  0.4516  0.4547  0.4578  0.4609  0.4639  0.4670  0.4700  0.4729  0.4758  0.4788  0.4817 
##  OTU233  OTU648  OTU240   OTU16   OTU92   OTU44  OTU491  OTU139  OTU220  OTU227  OTU278 
##  0.4846  0.4874  0.4903  0.4931  0.4959  0.4987  0.5014  0.5041  0.5068  0.5094  0.5121 
##  OTU101 OTU1089  OTU103  OTU171   OTU45  OTU253  OTU107  OTU261  OTU365  OTU206  OTU231 
##  0.5147  0.5172  0.5198  0.5222  0.5246  0.5270  0.5294  0.5317  0.5341  0.5363  0.5386 
## OTU1235   OTU41  OTU479 OTU1288 OTU1102   OTU81 OTU1050  OTU294  OTU181   OTU60  OTU945 
##  0.5409  0.5431  0.5453  0.5475  0.5497  0.5519  0.5540  0.5561  0.5581  0.5602  0.5622 
##   OTU82   OTU48  OTU360  OTU472  OTU471  OTU136  OTU169  OTU248  OTU315  OTU184  OTU638 
##  0.5642  0.5661  0.5681  0.5701  0.5721  0.5740  0.5759  0.5779  0.5798  0.5817  0.5836 
##  OTU182  OTU352  OTU716  OTU647  OTU191  OTU349  OTU457  OTU938 OTU1013  OTU751  OTU619 
##  0.5855  0.5874  0.5892  0.5911  0.5930  0.5948  0.5966  0.5984  0.6002  0.6020  0.6038 
##  OTU164  OTU166  OTU251  OTU190  OTU154  OTU434  OTU707 OTU1116 OTU1218  OTU302  OTU489 
##  0.6055  0.6073  0.6090  0.6107  0.6124  0.6141  0.6157  0.6174  0.6190  0.6207  0.6223 
##  OTU421  OTU582  OTU286    OTU7  OTU259  OTU667  OTU364  OTU456 OTU1319  OTU298  OTU992 
##  0.6240  0.6256  0.6272  0.6288  0.6304  0.6320  0.6336  0.6351  0.6367  0.6383  0.6398 
##  OTU562  OTU304  OTU468  OTU610  OTU170  OTU347  OTU939   OTU86  OTU430  OTU246  OTU239 
##  0.6413  0.6428  0.6443  0.6458  0.6473  0.6488  0.6503  0.6517  0.6531  0.6545  0.6558 
##  OTU949  OTU255  OTU439  OTU466  OTU132  OTU530  OTU579  OTU368  OTU362   OTU28  OTU451 
##  0.6572  0.6586  0.6599  0.6613  0.6626  0.6639  0.6652  0.6665  0.6678  0.6691  0.6704 
##  OTU653 OTU1299  OTU309  OTU333  OTU399  OTU323  OTU953  OTU348  OTU162 OTU1024  OTU389 
##  0.6717  0.6730  0.6743  0.6755  0.6768  0.6781  0.6793  0.6806  0.6818  0.6830  0.6842 
## OTU1005  OTU929  OTU524 OTU1100  OTU586   OTU95  OTU279  OTU799   OTU87 OTU1101  OTU232 
##  0.6854  0.6866  0.6878  0.6890  0.6901  0.6913  0.6924  0.6936  0.6947  0.6959  0.6970 
##  OTU725  OTU303  OTU999 
##  0.6981  0.6993  0.7004 
## 
## $`Hyperarid dolomite_Arid loess soil`
##   OTU1   OTU2   OTU3   OTU7   OTU5   OTU9  OTU18   OTU8  OTU17  OTU41 OTU936  OTU28 
##  0.132  0.206  0.279  0.331  0.360  0.382  0.402  0.421  0.440  0.457  0.471  0.484 
##  OTU12  OTU16  OTU10  OTU11  OTU20   OTU6  OTU15  OTU14  OTU13  OTU24  OTU33  OTU45 
##  0.497  0.509  0.521  0.533  0.543  0.553  0.562  0.571  0.579  0.586  0.593  0.601 
##  OTU88  OTU25  OTU54  OTU62  OTU34  OTU39  OTU47  OTU19  OTU22  OTU35  OTU68  OTU32 
##  0.608  0.615  0.622  0.628  0.634  0.640  0.646  0.651  0.656  0.660  0.664  0.668 
##  OTU29 OTU756  OTU23  OTU37  OTU78  OTU26 OTU177  OTU30 OTU133  OTU46 
##  0.671  0.675  0.678  0.682  0.685  0.689  0.692  0.695  0.698  0.702

Taxonomic features

Explore and plot the taxonomic distribution of the sequences

Let’s look at the agglomerated taxa

Rock_weathering_filt3_glom <- tax_glom(Rock_weathering_filt3_GMPR, 
                             "Phylum", 
                             NArm = TRUE)
Rock_weathering_filt3_glom_rel <- transform_sample_counts(Rock_weathering_filt3_glom, function(x) x / sum(x)) 
Rock_weathering_filt3_glom_rel_DF <- psmelt(Rock_weathering_filt3_glom_rel)
Rock_weathering_filt3_glom_rel_DF$Phylum %<>% as.character()

# group dataframe by Phylum, calculate median rel. abundance
Rock_weathering_filt3_glom_rel_DF %>%
  group_by(Phylum) %>%
  summarise(median = median(Abundance)) ->
  medians

# find Phyla whose rel. abund. is less than 0.5%
Rare_phyla <- medians[medians$median <= 0.005, ]$Phylum

# change their name to "Rare"
Rock_weathering_filt3_glom_rel_DF[Rock_weathering_filt3_glom_rel_DF$Phylum %in% Rare_phyla, ]$Phylum <- 'Rare'
# re-group
Rock_weathering_filt3_glom_rel_DF %>%
  group_by(Sample, Climate, Phylum, Rock.type, Source) %>%
  summarise(Abundance = sum(Abundance)) ->
  Rock_weathering_filt3_glom_rel_DF_2plot

# ab.taxonomy$Freq <- sqrt(ab.taxonomy$Freq)
Rock_weathering_filt3_glom_rel_DF_2plot$Phylum %<>% sub("unclassified", "Unclassified", .)
Rock_weathering_filt3_glom_rel_DF_2plot$Phylum %<>% sub("uncultured", "Unclassified", .)

Rock_weathering_filt3_glom_rel_DF_2plot %>% 
  group_by(Sample) %>% 
  filter(Phylum == "Rare") %>% 
  summarise(`Rares (%)` = sum(Abundance * 100)) -> 
  Rares
# Percentage of reads classified as rare 
Rares %>% 
  kable(., digits = 2, caption = "Percentage of reads per sample type classified as rare:") %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"), full_width = F)
Percentage of reads per sample type classified as rare:
Sample Rares (%)
SbDust1S14 2.05
SbDust2S31 0.43
SbSlp1SNW49 0.82
SbSlp2SNW50 0.91
SbSlp3SNW51 0.85
SbSlp4SNW52 1.99
SbSlp5SNW53 0.72
SbSlp6SNW54 1.59
SbSoil1SA10 9.80
SbSoil2SA11 10.41
SbSoil3SA12 9.62
SbWad1SNW55 2.90
SbWad2SNW56 0.48
SbWad3SNW57 0.42
SbWad4SNW58 0.20
SbWad5SNW59 0.47
SbWad6SNW60 1.47
UvDust1S32 1.60
UvDust2S33 0.12
UvSlp1GS70 1.09
UvSlp2GS71 0.42
UvSlp3CS25 0.77
UvSlp3GS72 0.85
UvSlp4GS73 0.64
UvSlp5GS74 0.72
UvSlp6GS75 0.34
UvWad1GS76 9.37
UvWad2CS23 2.17
UvWad2GS77 0.75
UvWad3CS27 2.96
UvWad3GS78 0.16
UvWad4GS79 0.77
UvWad5GS80 0.30
UvWad6GS81 0.38
Percentage of reads per sample type classified as rare:
Sample Rares (%)
SbDust1S14 2.05
UvWad2CS23 2.17
UvSlp3CS25 0.77
UvWad3CS27 2.96
SbDust2S31 0.43
UvDust1S32 1.60
UvDust2S33 0.12
SbSlp1SNW49 0.82
SbSlp2SNW50 0.91
SbSlp3SNW51 0.85
SbSlp4SNW52 1.99
SbSlp5SNW53 0.72
SbSlp6SNW54 1.59
SbWad1SNW55 2.90
SbWad2SNW56 0.48
SbWad3SNW57 0.42
SbWad4SNW58 0.20
SbWad5SNW59 0.47
SbWad6SNW60 1.47
UvSlp1GS70 1.09
UvSlp2GS71 0.42
UvSlp3GS72 0.85
UvSlp4GS73 0.64
UvSlp5GS74 0.72
UvSlp6GS75 0.34
UvWad1GS76 9.37
UvWad2GS77 0.75
UvWad3GS78 0.16
UvWad4GS79 0.77
UvWad5GS80 0.30
UvWad6GS81 0.38
SbSoil1SA10 9.80
SbSoil2SA11 10.41
SbSoil3SA12 9.62

Test differences between samples on the phylum level

## Taxonomy Table:     [1 taxa by 1 taxonomic ranks]:
##      Phylum          
## OTU1 "Proteobacteria"
## 
## DV:  Abundance 
## Observations:  34 
## D:  1 
## MS total:  99.2 
## 
##                Df Sum Sq     H p.value
## Climate         1   1118 11.28   0.001
## Source          3    412  4.16   0.245
## Climate:Source  1      2  0.02   0.876
## Residuals      28   1740              
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 5, p-value = 0.2
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -57.3  77.1
## sample estimates:
## difference in location 
##                  -8.84 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 10, p-value = 0.9
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -45.1  61.7
## sample estimates:
## difference in location 
##                   3.71 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 3, p-value = 0.04
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -44.6 -10.5
## sample estimates:
## difference in location 
##                  -24.3 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 20, p-value = 0.6
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -31.6  23.3
## sample estimates:
## difference in location 
##                   2.73 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 0, p-value = 0.04
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -92.7 -22.9
## sample estimates:
## difference in location 
##                  -41.8 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 20, p-value = 0.006
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -43.7 -14.5
## sample estimates:
## difference in location 
##                  -31.9 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 10, p-value = 0.4
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -9.15  9.09
## sample estimates:
## difference in location 
##                  -3.91 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 3, p-value = 0.04
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -43.18  -5.63
## sample estimates:
## difference in location 
##                  -35.8 
## 
## Taxonomy Table:     [1 taxa by 1 taxonomic ranks]:
##      Phylum               
## OTU2 "Deinococcus-Thermus"
## 
## DV:  Abundance 
## Observations:  34 
## D:  1 
## MS total:  99.2 
## 
##                Df Sum Sq     H p.value
## Climate         1    213  2.14   0.143
## Source          3   1475 14.87   0.002
## Climate:Source  1     73  0.73   0.392
## Residuals      28   1513              
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 20, p-value = 0.04
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##   0.116 28.897
## sample estimates:
## difference in location 
##                   3.76 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 0, p-value = 0.04
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -16.47  -6.63
## sample estimates:
## difference in location 
##                  -12.4 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 20, p-value = 1
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -8.04 20.82
## sample estimates:
## difference in location 
##                  0.132 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 30, p-value = 0.07
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -1.56 12.95
## sample estimates:
## difference in location 
##                   6.28 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 20, p-value = 0.04
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##   6.55 16.59
## sample estimates:
## difference in location 
##                     12 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 50, p-value = 0.3
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -11.37   7.95
## sample estimates:
## difference in location 
##                  -5.99 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 40, p-value = 0.02
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##   0.173 24.992
## sample estimates:
## difference in location 
##                   2.55 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 0, p-value = 0.01
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -16.14  -6.64
## sample estimates:
## difference in location 
##                    -12 
## 
## Taxonomy Table:     [1 taxa by 1 taxonomic ranks]:
##      Phylum         
## OTU5 "Bacteroidetes"
## 
## DV:  Abundance 
## Observations:  34 
## D:  1 
## MS total:  99.2 
## 
##                Df Sum Sq    H p.value
## Climate         1     13 0.13   0.718
## Source          3    822 8.29   0.040
## Climate:Source  1     75 0.75   0.385
## Residuals      28   2363             
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 5, p-value = 0.2
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -68.3  14.0
## sample estimates:
## difference in location 
##                  -27.2 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 20, p-value = 0.5
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -10.4  67.9
## sample estimates:
## difference in location 
##                   29.6 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 30, p-value = 0.3
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -1.35  4.99
## sample estimates:
## difference in location 
##                   2.07 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 30, p-value = 0.2
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -0.995 10.098
## sample estimates:
## difference in location 
##                   5.63 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 20, p-value = 0.08
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -0.822 15.087
## sample estimates:
## difference in location 
##                   7.65 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 50, p-value = 0.2
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -6.95  1.65
## sample estimates:
## difference in location 
##                  -3.57 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 10, p-value = 0.3
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -5.57  2.83
## sample estimates:
## difference in location 
##                  -1.64 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 20, p-value = 0.7
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -7.94  5.14
## sample estimates:
## difference in location 
##                  -2.62 
## 
## Taxonomy Table:     [1 taxa by 1 taxonomic ranks]:
##      Phylum         
## OTU7 "Cyanobacteria"
## 
## DV:  Abundance 
## Observations:  34 
## D:  1 
## MS total:  99.2 
## 
##                Df Sum Sq    H p.value
## Climate         1     96 0.96   0.326
## Source          3    853 8.61   0.035
## Climate:Source  1     45 0.45   0.500
## Residuals      28   2278             
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 20, p-value = 0.2
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -2.46 18.18
## sample estimates:
## difference in location 
##                   2.96 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 6, p-value = 0.3
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -37.74   2.34
## sample estimates:
## difference in location 
##                  -3.32 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 30, p-value = 0.04
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##   0.671 18.087
## sample estimates:
## difference in location 
##                   3.08 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 30, p-value = 0.03
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##   0.0686 24.4378
## sample estimates:
## difference in location 
##                   5.27 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 20, p-value = 0.2
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -0.591 37.801
## sample estimates:
## difference in location 
##                   4.21 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 70, p-value = 1
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -7.31  5.10
## sample estimates:
## difference in location 
##                  0.263 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 20, p-value = 0.7
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -3.69 14.61
## sample estimates:
## difference in location 
##                   1.13 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 20, p-value = 0.8
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -23.70   4.78
## sample estimates:
## difference in location 
##                  -1.64 
## 
## Taxonomy Table:     [1 taxa by 1 taxonomic ranks]:
##       Phylum         
## OTU10 "Acidobacteria"
## 
## DV:  Abundance 
## Observations:  34 
## D:  1 
## MS total:  99.2 
## 
##                Df Sum Sq     H p.value
## Climate         1      2  0.02   0.877
## Source          3   1299 13.10   0.004
## Climate:Source  1     70  0.71   0.399
## Residuals      28   1900              
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 10, p-value = 0.8
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -1.88  6.84
## sample estimates:
## difference in location 
##                   -0.2 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 5, p-value = 0.2
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -3.89  1.99
## sample estimates:
## difference in location 
##                  -1.61 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 20, p-value = 1
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -1.44  1.14
## sample estimates:
## difference in location 
##               -0.00745 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 30, p-value = 0.2
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -0.80  2.81
## sample estimates:
## difference in location 
##                   1.67 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 20, p-value = 0.1
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -0.874  4.205
## sample estimates:
## difference in location 
##                   2.41 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 40, p-value = 0.07
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -2.538  0.111
## sample estimates:
## difference in location 
##                  -1.54 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 0, p-value = 0.01
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -10.42  -7.39
## sample estimates:
## difference in location 
##                   -8.6 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 40, p-value = 0.01
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  5.71 9.48
## sample estimates:
## difference in location 
##                   7.04 
## 
## Taxonomy Table:     [1 taxa by 1 taxonomic ranks]:
##       Phylum          
## OTU20 "Actinobacteria"
## 
## DV:  Abundance 
## Observations:  34 
## D:  1 
## MS total:  99.2 
## 
##                Df Sum Sq    H p.value
## Climate         1    585 5.90   0.015
## Source          3    761 7.68   0.053
## Climate:Source  1      1 0.01   0.928
## Residuals      28   1926             
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 20, p-value = 0.08
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -10.1  56.9
## sample estimates:
## difference in location 
##                   36.9 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 7, p-value = 0.4
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -44.93   9.86
## sample estimates:
## difference in location 
##                  -6.64 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 30, p-value = 0.1
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -6.17 41.74
## sample estimates:
## difference in location 
##                   20.6 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 10, p-value = 0.6
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -23.1  22.2
## sample estimates:
## difference in location 
##                  -6.06 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 20, p-value = 0.06
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -1.95 46.69
## sample estimates:
## difference in location 
##                   12.8 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 100, p-value = 0.01
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##   7.17 40.31
## sample estimates:
## difference in location 
##                   21.9 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 30, p-value = 0.2
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -11.0  33.2
## sample estimates:
## difference in location 
##                   17.1 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 20, p-value = 0.3
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -19.1  25.8
## sample estimates:
## difference in location 
##                   15.9 
## 
## Taxonomy Table:     [1 taxa by 1 taxonomic ranks]:
##       Phylum      
## OTU44 "Firmicutes"
## 
## DV:  Abundance 
## Observations:  34 
## D:  1 
## MS total:  99.2 
## 
##                Df Sum Sq     H p.value
## Climate         1    362  3.65   0.056
## Source          3   1570 15.83   0.001
## Climate:Source  1      1  0.01   0.917
## Residuals      28   1339              
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 0, p-value = 0.04
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -8.62 -4.81
## sample estimates:
## difference in location 
##                  -6.67 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 20, p-value = 0.04
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  0.255 8.721
## sample estimates:
## difference in location 
##                   5.34 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 2, p-value = 0.03
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -1.0126 -0.0336
## sample estimates:
## difference in location 
##                 -0.346 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 20, p-value = 0.5
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -0.667  2.129
## sample estimates:
## difference in location 
##                  0.128 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 8, p-value = 0.5
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -2.27  4.52
## sample estimates:
## difference in location 
##                 -0.205 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 20, p-value = 0.003
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -1.661 -0.204
## sample estimates:
## difference in location 
##                 -0.533 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 0, p-value = 0.01
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -1.267 -0.788
## sample estimates:
## difference in location 
##                 -0.998 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 20, p-value = 0.6
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -1.51  1.12
## sample estimates:
## difference in location 
##                  0.522 
## 
## Taxonomy Table:     [1 taxa by 1 taxonomic ranks]:
##        Phylum            
## OTU116 "Gemmatimonadetes"
## 
## DV:  Abundance 
## Observations:  34 
## D:  1 
## MS total:  99.2 
## 
##                Df Sum Sq     H p.value
## Climate         1    536  5.41   0.020
## Source          3   1335 13.46   0.004
## Climate:Source  1     29  0.30   0.586
## Residuals      28   1372              
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 10, p-value = 0.9
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -1.56  2.37
## sample estimates:
## difference in location 
##                   0.11 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 10, p-value = 0.8
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -0.594  1.579
## sample estimates:
## difference in location 
##                  0.493 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 5, p-value = 0.07
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -2.630  0.147
## sample estimates:
## difference in location 
##                  -1.28 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 0, p-value = 0.01
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -2.865 -0.966
## sample estimates:
## difference in location 
##                  -1.92 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 20, p-value = 0.1
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -0.115  0.699
## sample estimates:
## difference in location 
##                  0.226 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 100, p-value = 0.01
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  0.068 1.125
## sample estimates:
## difference in location 
##                  0.575 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 0, p-value = 0.01
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -5.20 -3.07
## sample estimates:
## difference in location 
##                  -4.26 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 40, p-value = 0.01
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  4.37 5.33
## sample estimates:
## difference in location 
##                   4.96 
## 
## Taxonomy Table:     [1 taxa by 1 taxonomic ranks]:
##        Phylum       
## OTU225 "Chloroflexi"
## 
## DV:  Abundance 
## Observations:  34 
## D:  1 
## MS total:  99.2 
## 
##                Df Sum Sq     H p.value
## Climate         1    725  7.31   0.007
## Source          3   1423 14.35   0.002
## Climate:Source  1     17  0.17   0.678
## Residuals      28   1107              
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 20, p-value = 0.08
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -1.89 27.56
## sample estimates:
## difference in location 
##                   8.69 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 10, p-value = 0.9
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -2.39  2.13
## sample estimates:
## difference in location 
##                  0.262 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 6, p-value = 0.1
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -19.84   5.73
## sample estimates:
## difference in location 
##                   -8.4 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 0, p-value = 0.01
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -23.2 -10.8
## sample estimates:
## difference in location 
##                  -21.1 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 10, p-value = 0.8
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -1.82  2.77
## sample estimates:
## difference in location 
##                  0.134 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 100, p-value = 5e-04
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##   4.22 15.57
## sample estimates:
## difference in location 
##                   8.96 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 10, p-value = 0.3
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -15.09   6.63
## sample estimates:
## difference in location 
##                  -6.71 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 40, p-value = 0.01
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  13.7 17.4
## sample estimates:
## difference in location 
##                   16.2
## Taxonomy Table:     [1 taxa by 1 taxonomic ranks]:
##      Order            
## OTU1 "Burkholderiales"
## 
## DV:  Abundance 
## Observations:  34 
## D:  1 
## MS total:  99.2 
## 
##                Df Sum Sq     H p.value
## Climate         1    901  9.08   0.003
## Source          3   1292 13.03   0.005
## Climate:Source  1     79  0.80   0.371
## Residuals      28   1000              
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 0, p-value = 0.04
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -41.19  -3.36
## sample estimates:
## difference in location 
##                  -22.3 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 10, p-value = 1
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -40.2  41.2
## sample estimates:
## difference in location 
##                 -0.356 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 2, p-value = 0.03
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -0.455 -0.126
## sample estimates:
## difference in location 
##                 -0.297 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 30, p-value = 0.04
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  12.7 40.8
## sample estimates:
## difference in location 
##                   34.2 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 20, p-value = 0.08
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -0.325 43.909
## sample estimates:
## difference in location 
##                   34.8 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 9, p-value = 3e-04
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -36.5 -15.3
## sample estimates:
## difference in location 
##                  -34.7 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 0, p-value = 0.01
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -1.134 -0.393
## sample estimates:
## difference in location 
##                 -0.743 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 3, p-value = 0.04
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -40.5 -12.0
## sample estimates:
## difference in location 
##                  -33.8 
## 
## Taxonomy Table:     [1 taxa by 1 taxonomic ranks]:
##      Order               
## OTU5 "Sphingobacteriales"
## 
## DV:  Abundance 
## Observations:  34 
## D:  1 
## MS total:  99.2 
## 
##                Df Sum Sq     H p.value
## Climate         1    184  1.85   0.174
## Source          3   1245 12.56   0.006
## Climate:Source  1     11  0.11   0.736
## Residuals      28   1833              
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 8, p-value = 0.5
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -1.110  0.842
## sample estimates:
## difference in location 
##                 -0.309 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 5, p-value = 0.2
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -13.08   1.13
## sample estimates:
## difference in location 
##                  -6.12 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 20, p-value = 0.7
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -0.664  0.764
## sample estimates:
## difference in location 
##                 0.0192 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 30, p-value = 0.1
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -0.51 10.25
## sample estimates:
## difference in location 
##                   6.49 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 20, p-value = 0.1
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -0.478 13.695
## sample estimates:
## difference in location 
##                   6.46 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 20, p-value = 0.005
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -8.517 -0.853
## sample estimates:
## difference in location 
##                  -6.14 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 0, p-value = 0.01
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -4.33 -1.61
## sample estimates:
## difference in location 
##                     -3 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 10, p-value = 0.6
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -8.04  3.57
## sample estimates:
## difference in location 
##                  -3.72 
## 
## Taxonomy Table:     [1 taxa by 1 taxonomic ranks]:
##      Order              
## OTU6 "Enterobacteriales"
## 
## DV:  Abundance 
## Observations:  34 
## D:  1 
## MS total:  99.2 
## 
##                Df Sum Sq     H p.value
## Climate         1     71  0.71   0.399
## Source          3   2314 23.34   0.000
## Climate:Source  1     64  0.65   0.422
## Residuals      28    824              
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 20, p-value = 0.2
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -0.571 91.132
## sample estimates:
## difference in location 
##                   3.55 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 20, p-value = 0.08
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -10.44   1.91
## sample estimates:
## difference in location 
##                   1.69 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 3, p-value = 0.04
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -49.50  -5.47
## sample estimates:
## difference in location 
##                  -21.5 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 0, p-value = 0.01
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -51.7 -22.4
## sample estimates:
## difference in location 
##                  -26.6 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 0, p-value = 0.04
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -84.2 -55.6
## sample estimates:
## difference in location 
##                  -69.9 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 100, p-value = 4e-04
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  1.93 8.06
## sample estimates:
## difference in location 
##                   5.11 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 40, p-value = 0.01
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##   1.3 17.4
## sample estimates:
## difference in location 
##                   5.33 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 8, p-value = 0.2
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -0.5844  0.0654
## sample estimates:
## difference in location 
##                 -0.103 
## 
## Taxonomy Table:     [1 taxa by 1 taxonomic ranks]:
##      Order         
## OTU7 "SubsectionII"
## 
## DV:  Abundance 
## Observations:  34 
## D:  1 
## MS total:  99.2 
## 
##                Df Sum Sq    H p.value
## Climate         1    174 1.76   0.185
## Source          3    717 7.23   0.065
## Climate:Source  1      2 0.02   0.876
## Residuals      28   2379             
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 10, p-value = 0.8
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -1.05 14.87
## sample estimates:
## difference in location 
##                  0.195 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 4, p-value = 0.2
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -33.557   0.854
## sample estimates:
## difference in location 
##                  -3.54 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 20, p-value = 0.6
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -0.27  9.15
## sample estimates:
## difference in location 
##                  0.518 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 30, p-value = 0.03
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##   0.158 23.911
## sample estimates:
## difference in location 
##                   4.18 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 20, p-value = 0.1
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -0.365 33.663
## sample estimates:
## difference in location 
##                   4.55 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 40, p-value = 0.08
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -8.94  1.06
## sample estimates:
## difference in location 
##                  -1.22 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 20, p-value = 0.8
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -0.578  9.034
## sample estimates:
## difference in location 
##                  0.213 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 5, p-value = 0.07
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -23.792   0.123
## sample estimates:
## difference in location 
##                  -3.89 
## 
## Taxonomy Table:     [1 taxa by 1 taxonomic ranks]:
##      Order        
## OTU9 "Rhizobiales"
## 
## DV:  Abundance 
## Observations:  34 
## D:  1 
## MS total:  99.2 
## 
##                Df Sum Sq    H p.value
## Climate         1    880 8.88  0.0029
## Source          3    547 5.51  0.1378
## Climate:Source  1    400 4.04  0.0445
## Residuals      28   1445             
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 9, p-value = 0.6
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -5.88  2.95
## sample estimates:
## difference in location 
##                  -1.47 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 6, p-value = 0.3
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -11.7   5.9
## sample estimates:
## difference in location 
##                  -3.36 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 20, p-value = 0.8
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -1.99  1.21
## sample estimates:
## difference in location 
##                 -0.501 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 30, p-value = 0.04
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##   0.976 10.129
## sample estimates:
## difference in location 
##                   4.44 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 8, p-value = 0.5
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -7.85  4.60
## sample estimates:
## difference in location 
##                   -1.2 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 10, p-value = 6e-04
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -7.32 -2.45
## sample estimates:
## difference in location 
##                  -4.61 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 0, p-value = 0.01
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -4.59 -2.23
## sample estimates:
## difference in location 
##                  -2.96 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 10, p-value = 0.4
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -7.27  1.28
## sample estimates:
## difference in location 
##                  -1.61 
## 
## Taxonomy Table:     [1 taxa by 1 taxonomic ranks]:
##       Order       
## OTU10 "Subgroup_4"
## 
## DV:  Abundance 
## Observations:  34 
## D:  1 
## MS total:  99.2 
## 
##                Df Sum Sq     H p.value
## Climate         1     49  0.50   0.480
## Source          3   1315 13.26   0.004
## Climate:Source  1     73  0.73   0.392
## Residuals      28   1836              
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 10, p-value = 0.8
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -1.36  6.53
## sample estimates:
## difference in location 
##                 -0.113 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 4, p-value = 0.2
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -4.08  1.32
## sample estimates:
## difference in location 
##                  -1.91 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 10, p-value = 0.6
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -0.940  0.823
## sample estimates:
## difference in location 
##                 -0.265 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 30, p-value = 0.1
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -0.556  3.246
## sample estimates:
## difference in location 
##                   1.83 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 20, p-value = 0.08
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -0.612  4.277
## sample estimates:
## difference in location 
##                    2.4 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 30, p-value = 0.02
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -2.847 -0.324
## sample estimates:
## difference in location 
##                  -1.97 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 3, p-value = 0.04
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -6.17 -4.03
## sample estimates:
## difference in location 
##                   -5.3 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 40, p-value = 0.01
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  1.72 5.44
## sample estimates:
## difference in location 
##                   3.04 
## 
## Taxonomy Table:     [1 taxa by 1 taxonomic ranks]:
##       Order             
## OTU11 "Sphingomonadales"
## 
## DV:  Abundance 
## Observations:  34 
## D:  1 
## MS total:  99.2 
## 
##                Df Sum Sq    H p.value
## Climate         1      5 0.05   0.823
## Source          3    596 6.01   0.111
## Climate:Source  1     66 0.67   0.414
## Residuals      28   2605             
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 10, p-value = 0.9
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -5.9 12.8
## sample estimates:
## difference in location 
##                   0.14 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 9, p-value = 0.6
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -8.33  4.10
## sample estimates:
## difference in location 
##                  -0.71 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 20, p-value = 0.8
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -4.13  5.96
## sample estimates:
## difference in location 
##                  0.326 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 20, p-value = 0.6
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -3.23  5.51
## sample estimates:
## difference in location 
##                   1.48 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 20, p-value = 0.04
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  1.38 9.58
## sample estimates:
## difference in location 
##                   5.12 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 50, p-value = 0.3
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -3.50  1.09
## sample estimates:
## difference in location 
##                   -1.2 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 10, p-value = 0.4
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -4.00  3.07
## sample estimates:
## difference in location 
##                  -1.06 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 20, p-value = 0.7
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -2.89  2.81
## sample estimates:
## difference in location 
##                 -0.536 
## 
## Taxonomy Table:     [1 taxa by 1 taxonomic ranks]:
##       Order            
## OTU14 "Caulobacterales"
## 
## DV:  Abundance 
## Observations:  34 
## D:  1 
## MS total:  99.2 
## 
##                Df Sum Sq     H p.value
## Climate         1    431  4.34   0.037
## Source          3   1248 12.58   0.006
## Climate:Source  1     40  0.40   0.525
## Residuals      28   1554              
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 9, p-value = 0.6
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -1.070  0.948
## sample estimates:
## difference in location 
##                -0.0385 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 5, p-value = 0.2
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -2.937  0.523
## sample estimates:
## difference in location 
##                 -0.849 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 20, p-value = 0.4
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -0.431  0.773
## sample estimates:
## difference in location 
##                  0.218 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 40, p-value = 0.02
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  0.281 2.461
## sample estimates:
## difference in location 
##                    1.1 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 20, p-value = 0.04
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  0.365 3.036
## sample estimates:
## difference in location 
##                   1.29 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 20, p-value = 0.002
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -1.680 -0.436
## sample estimates:
## difference in location 
##                 -0.921 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 20, p-value = 1
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -0.432  0.364
## sample estimates:
## difference in location 
##                 0.0155 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 0, p-value = 0.01
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -2.0437 -0.0965
## sample estimates:
## difference in location 
##                 -0.961 
## 
## Taxonomy Table:     [1 taxa by 1 taxonomic ranks]:
##       Order            
## OTU20 "Rubrobacterales"
## 
## DV:  Abundance 
## Observations:  34 
## D:  1 
## MS total:  99.2 
## 
##                Df Sum Sq     H p.value
## Climate         1    362  3.65   0.056
## Source          3   1284 12.94   0.005
## Climate:Source  1      7  0.07   0.785
## Residuals      28   1619              
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 20, p-value = 0.04
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##   1.34 35.28
## sample estimates:
## difference in location 
##                   23.1 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 0, p-value = 0.04
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -34.3941  -0.0705
## sample estimates:
## difference in location 
##                   -4.2 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 30, p-value = 0.04
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##   5.21 27.55
## sample estimates:
## difference in location 
##                   17.1 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 20, p-value = 0.9
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -6.01 27.58
## sample estimates:
## difference in location 
##                  0.306 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 20, p-value = 0.06
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -0.173 34.359
## sample estimates:
## difference in location 
##                   4.64 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 100, p-value = 0.03
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##   1.01 21.60
## sample estimates:
## difference in location 
##                   13.5 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 30, p-value = 0.04
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##   5.38 24.82
## sample estimates:
## difference in location 
##                   16.8 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 20, p-value = 0.8
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -26.41   5.86
## sample estimates:
## difference in location 
##                    1.7 
## 
## Taxonomy Table:     [1 taxa by 1 taxonomic ranks]:
##       Order         
## OTU22 "Cytophagales"
## 
## DV:  Abundance 
## Observations:  34 
## D:  1 
## MS total:  99.2 
## 
##                Df Sum Sq     H p.value
## Climate         1   1165 11.75   0.001
## Source          3     45  0.45   0.929
## Climate:Source  1    282  2.84   0.092
## Residuals      28   1781              
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 7, p-value = 0.4
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -67.5  13.8
## sample estimates:
## difference in location 
##                  -27.7 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 20, p-value = 0.04
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##   1.0 67.3
## sample estimates:
## difference in location 
##                   34.9 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 30, p-value = 0.3
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -1.07  5.47
## sample estimates:
## difference in location 
##                   1.34 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 10, p-value = 0.3
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -1.839  0.353
## sample estimates:
## difference in location 
##                 -0.279 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 20, p-value = 0.06
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -0.195  1.887
## sample estimates:
## difference in location 
##                  0.542 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 100, p-value = 0.01
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  0.416 3.920
## sample estimates:
## difference in location 
##                   2.37 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 20, p-value = 0.5
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -1.87  5.15
## sample estimates:
## difference in location 
##                   1.17 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 30, p-value = 0.03
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  0.155 1.863
## sample estimates:
## difference in location 
##                   1.16 
## 
## Taxonomy Table:     [1 taxa by 1 taxonomic ranks]:
##       Order          
## OTU30 "Micrococcales"
## 
## DV:  Abundance 
## Observations:  34 
## D:  1 
## MS total:  99.2 
## 
##                Df Sum Sq    H p.value
## Climate         1    265 2.68   0.102
## Source          3    750 7.56   0.056
## Climate:Source  1     34 0.34   0.560
## Residuals      28   2223             
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 2, p-value = 0.08
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -5.21  4.63
## sample estimates:
## difference in location 
##                  -2.18 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 20, p-value = 0.08
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -3.83  5.11
## sample estimates:
## difference in location 
##                   2.14 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 20, p-value = 0.3
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -0.532  1.477
## sample estimates:
## difference in location 
##                  0.271 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 30, p-value = 0.3
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -0.512  0.787
## sample estimates:
## difference in location 
##                  0.203 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 7, p-value = 0.4
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -0.50  5.57
## sample estimates:
## difference in location 
##                 -0.228 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 80, p-value = 0.7
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -0.220  0.369
## sample estimates:
## difference in location 
##                 0.0729 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 4, p-value = 0.05
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -1.526  0.268
## sample estimates:
## difference in location 
##                  -1.07 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 30, p-value = 0.04
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  0.422 1.495
## sample estimates:
## difference in location 
##                    1.1 
## 
## Taxonomy Table:     [1 taxa by 1 taxonomic ranks]:
##       Order       
## OTU33 "Frankiales"
## 
## DV:  Abundance 
## Observations:  34 
## D:  1 
## MS total:  99.2 
## 
##                Df Sum Sq     H p.value
## Climate         1     65 0.655   0.418
## Source          3    263 2.649   0.449
## Climate:Source  1     10 0.097   0.756
## Residuals      28   2935              
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 20, p-value = 0.5
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -2.72 10.23
## sample estimates:
## difference in location 
##                   1.11 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 8, p-value = 0.5
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -6.78  2.83
## sample estimates:
## difference in location 
##                 -0.485 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 20, p-value = 0.9
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -1.57  4.26
## sample estimates:
## difference in location 
##                  0.298 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 20, p-value = 0.8
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -2.58  4.27
## sample estimates:
## difference in location 
##                 -0.159 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 20, p-value = 0.3
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -0.786  6.561
## sample estimates:
## difference in location 
##                   1.09 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 80, p-value = 0.6
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -1.37  1.65
## sample estimates:
## difference in location 
##                  0.569 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 20, p-value = 0.9
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -1.02  2.98
## sample estimates:
## difference in location 
##                 -0.136 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 20, p-value = 0.7
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -4.11  1.80
## sample estimates:
## difference in location 
##                  0.471 
## 
## Taxonomy Table:     [1 taxa by 1 taxonomic ranks]:
##       Order          
## OTU40 "Deinococcales"
## 
## DV:  Abundance 
## Observations:  34 
## D:  1 
## MS total:  99.2 
## 
##                Df Sum Sq     H p.value
## Climate         1     25  0.25   0.617
## Source          3   1445 14.57   0.002
## Climate:Source  1    191  1.92   0.165
## Residuals      28   1612              
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 20, p-value = 0.04
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##   0.116 28.897
## sample estimates:
## difference in location 
##                   3.76 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 0, p-value = 0.04
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -7.347 -0.588
## sample estimates:
## difference in location 
##                   -2.5 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 20, p-value = 1
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -8.04 20.82
## sample estimates:
## difference in location 
##                  0.132 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 10, p-value = 0.3
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -7.13  2.03
## sample estimates:
## difference in location 
##                  -2.58 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 20, p-value = 0.04
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  0.514 7.469
## sample estimates:
## difference in location 
##                   2.25 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 80, p-value = 0.6
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -1.34 18.29
## sample estimates:
## difference in location 
##                  0.952 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 40, p-value = 0.01
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##   0.324 25.016
## sample estimates:
## difference in location 
##                   2.69 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 0, p-value = 0.01
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -4.205 -0.685
## sample estimates:
## difference in location 
##                  -2.53 
## 
## Taxonomy Table:     [1 taxa by 1 taxonomic ranks]:
##       Order                
## OTU73 "Solirubrobacterales"
## 
## DV:  Abundance 
## Observations:  34 
## D:  1 
## MS total:  99.2 
## 
##                Df Sum Sq     H p.value
## Climate         1    671  6.76   0.009
## Source          3   1220 12.31   0.006
## Climate:Source  1      4  0.04   0.846
## Residuals      28   1378              
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 20, p-value = 0.08
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -0.235 27.641
## sample estimates:
## difference in location 
##                   4.61 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 5, p-value = 0.2
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -2.935  0.261
## sample estimates:
## difference in location 
##                 -0.496 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 20, p-value = 1
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -4.21  9.96
## sample estimates:
## difference in location 
##                 -0.109 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 0, p-value = 0.01
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -5.29 -2.54
## sample estimates:
## difference in location 
##                  -4.22 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 20, p-value = 0.1
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -0.176  3.066
## sample estimates:
## difference in location 
##                  0.575 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 100, p-value = 0.002
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##   1.2 11.8
## sample estimates:
## difference in location 
##                   3.69 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 20, p-value = 0.9
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -4.31 10.07
## sample estimates:
## difference in location 
##                  -0.42 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 40, p-value = 0.01
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  2.04 5.95
## sample estimates:
## difference in location 
##                   3.96 
## 
## Taxonomy Table:     [1 taxa by 1 taxonomic ranks]:
##        Order             
## OTU144 "Acidimicrobiales"
## 
## DV:  Abundance 
## Observations:  34 
## D:  1 
## MS total:  99.2 
## 
##                Df Sum Sq     H p.value
## Climate         1    403  4.06   0.044
## Source          3   1958 19.75   0.000
## Climate:Source  1     37  0.37   0.542
## Residuals      28    875              
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 20, p-value = 0.08
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -0.277  1.875
## sample estimates:
## difference in location 
##                  0.947 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 8, p-value = 0.5
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -0.695  0.294
## sample estimates:
## difference in location 
##                -0.0769 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 0, p-value = 0.01
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -8.06 -2.73
## sample estimates:
## difference in location 
##                  -4.38 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 0, p-value = 0.01
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -8.81 -3.72
## sample estimates:
## difference in location 
##                  -5.31 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 20, p-value = 0.06
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -0.00973  0.70729
## sample estimates:
## difference in location 
##                  0.204 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 100, p-value = 0.002
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  0.352 1.165
## sample estimates:
## difference in location 
##                  0.871 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 0, p-value = 0.01
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -3.35 -1.70
## sample estimates:
## difference in location 
##                  -2.35 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 40, p-value = 0.01
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  2.65 4.11
## sample estimates:
## difference in location 
##                   3.12 
## 
## Taxonomy Table:     [1 taxa by 1 taxonomic ranks]:
##        Order         
## OTU225 "JG30-KF-CM45"
## 
## DV:  Abundance 
## Observations:  34 
## D:  1 
## MS total:  99.2 
## 
##                Df Sum Sq     H p.value
## Climate         1   1051 10.59   0.001
## Source          3   1026 10.34   0.016
## Climate:Source  1     20  0.21   0.650
## Residuals      28   1176              
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 20, p-value = 0.08
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -0.335 20.313
## sample estimates:
## difference in location 
##                   4.14 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 10, p-value = 0.8
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -0.407  0.404
## sample estimates:
## difference in location 
##                0.00744 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 20, p-value = 0.9
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -3.84 11.81
## sample estimates:
## difference in location 
##                  0.601 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 0, p-value = 0.01
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -4.80 -2.22
## sample estimates:
## difference in location 
##                  -4.48 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 20, p-value = 0.3
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -0.155  0.514
## sample estimates:
## difference in location 
##                  0.116 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 100, p-value = 3e-04
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  2.58 6.32
## sample estimates:
## difference in location 
##                   4.24 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 20, p-value = 0.9
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -3.81 10.33
## sample estimates:
## difference in location 
##                 -0.209 
## 
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Abundance by Climate.Source
## W = 40, p-value = 0.01
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  3.70 4.57
## sample estimates:
## difference in location 
##                    4.3

Ternary plots

For the arid samples

Rock_weathering_filt3_GMPR_Arid_rel <- transform_sample_counts(Rock_weathering_filt3_GMPR_Arid, function(x) x / sum(x) ) # rel abundance
Rock_weathering_filt3_GMPR_Arid_merged <- merge_samples(Rock_weathering_filt3_GMPR_Arid_rel, "Source", fun = mean) # merge by source
Rock_weathering_filt3_GMPR_Arid_merged_rel <- transform_sample_counts(Rock_weathering_filt3_GMPR_Arid_merged, function(x) x / sum(x) ) # rel abundance per source
meandf <- as(otu_table(Rock_weathering_filt3_GMPR_Arid_merged_rel), "matrix")
if (!taxa_are_rows(Rock_weathering_filt3_GMPR_Arid_merged_rel)) { meandf <- t(meandf) }
abundance <- rowSums(meandf) / sum(meandf) * 100

Arid4Ternary <- data.frame(
  meandf,
  Abundance = abundance,
  Phylum = tax_table(Rock_weathering_filt3_GMPR_Arid_merged_rel)[, "Phylum"]
)
# Arid4Ternary <- dplyr::rename(Arid4Ternary, Loess_soil = Loess.soil)

Arid4Ternary$Phylum <-
  factor(Arid4Ternary$Phylum, levels = c(levels(Arid4Ternary$Phylum), 'Rare'))
Arid4Ternary$Phylum[Arid4Ternary$Phylum %in% Rare_phyla]  <- "Rare"
Arid4Ternary$Phylum %<>% 
  factor(., levels = Taxa_rank$Phylum) %>% 
  fct_relevel(., "Rare", after = Inf)

p_ternary_arid <-
  ggtern(data = Arid4Ternary,
         aes(
           x = Loess.soil,
           y = Dust,
           z = Limestone,
           size = Abundance,
           colour = Phylum
         )) +
  geom_point(alpha = 1 / 2) +
  scale_size(
    range = c(1, 5),
    name = "Abundance (%)"
  ) +
  theme_arrownormal() +
    scale_color_manual(values = pal("d3js")) +
  guides(colour = guide_legend(override.aes = list(size = 3))) +
  labs(x = "Loess soil") + 
  theme(axis.title = element_blank())
print(p_ternary_arid)

For the hyperarid samples

Rock_weathering_filt3_GMPR_Hyperarid_rel <- transform_sample_counts(Rock_weathering_filt3_GMPR_Hyperarid, function(x) x / sum(x) ) # rel abundance
Rock_weathering_filt3_GMPR_Hyperarid_merged <- merge_samples(Rock_weathering_filt3_GMPR_Hyperarid_rel, "Source", fun = mean) # merge by source
Rock_weathering_filt3_GMPR_Hyperarid_merged_rel <- transform_sample_counts(Rock_weathering_filt3_GMPR_Hyperarid_merged, function(x) x / sum(x) ) # rel abundance per source
meandf <- as(otu_table(Rock_weathering_filt3_GMPR_Hyperarid_merged_rel), "matrix")
if (!taxa_are_rows(Rock_weathering_filt3_GMPR_Hyperarid_merged_rel)) { meandf <- t(meandf) }
abundance <- rowSums(meandf) / sum(meandf) * 100

Hyperarid4Ternary <- data.frame(
  meandf,
  Abundance = abundance,
  Phylum = tax_table(Rock_weathering_filt3_GMPR_Hyperarid_merged_rel)[, "Phylum"]
)

Hyperarid4Ternary$Phylum <-
  factor(Hyperarid4Ternary$Phylum, levels = c(levels(Hyperarid4Ternary$Phylum), 'Rare'))
Hyperarid4Ternary$Phylum[Hyperarid4Ternary$Phylum %in% Rare_phyla]  <- "Rare"
Hyperarid4Ternary$Phylum %<>% 
  factor(., levels = Taxa_rank$Phylum) %>% 
  fct_relevel(., "Rare", after = Inf)

p_ternary_hyperarid <-
  ggtern(data = Hyperarid4Ternary,
         aes(
           x = Loess.soil,
           y = Dust,
           z = Dolomite,
           size = Abundance,
           colour = Phylum
         )) +
  geom_point(alpha = 1 / 2) +
  scale_size(
    range = c(1, 5),
    name = "Abundance (%)"
  ) +
  theme_arrownormal() +
  scale_color_manual(values = pal("d3js")) +
  guides(colour = guide_legend(override.aes = list(size = 3))) +
  labs(x = "Loess soil") + 
  theme(axis.title = element_blank())
print(p_ternary_hyperarid)

Differential abundance models

Detect differentially abundant OTUs using ALDEx2 (Fernandes et al. n.d.)

## [1] "multicore environment is is OK -- using the BiocParallel package"
## [1] "removed rows with sums equal to zero"
## [1] "computing iqlr centering"
## [1] "data format is OK"
## [1] "dirichlet samples complete"
## [1] "clr transformation complete"
## [1] "multicore environment is OK -- using the BiocParallel package"
## [1] "running tests for each MC instance:"
## |------------(25%)----------(50%)----------(75%)----------|
## [1] "multicore environment is is OK -- using the BiocParallel package"
## [1] "removed rows with sums equal to zero"
## [1] "computing iqlr centering"
## [1] "data format is OK"
## [1] "dirichlet samples complete"
## [1] "clr transformation complete"
## [1] "running tests for each MC instance:"
## |------------(25%)----------(50%)----------(75%)----------|
## [1] "multicore environment is OK -- using the BiocParallel package"
## [1] "sanity check complete"
## [1] "rab.all  complete"
## [1] "rab.win  complete"
## [1] "rab of samples complete"
## [1] "within sample difference calculated"
## [1] "between group difference calculated"
## [1] "group summaries calculated"
## [1] "effect size calculated"
## [1] "summarizing output"

## [1] "multicore environment is is OK -- using the BiocParallel package"
## [1] "removed rows with sums equal to zero"
## [1] "computing iqlr centering"
## [1] "data format is OK"
## [1] "dirichlet samples complete"
## [1] "clr transformation complete"
## [1] "running tests for each MC instance:"
## |------------(25%)----------(50%)----------(75%)----------|
## [1] "multicore environment is OK -- using the BiocParallel package"
## [1] "sanity check complete"
## [1] "rab.all  complete"
## [1] "rab.win  complete"
## [1] "rab of samples complete"
## [1] "within sample difference calculated"
## [1] "between group difference calculated"
## [1] "group summaries calculated"
## [1] "effect size calculated"
## [1] "summarizing output"

## [1] "multicore environment is is OK -- using the BiocParallel package"
## [1] "removed rows with sums equal to zero"
## [1] "computing iqlr centering"
## [1] "data format is OK"
## [1] "dirichlet samples complete"
## [1] "clr transformation complete"
## [1] "running tests for each MC instance:"
## |------------(25%)----------(50%)----------(75%)----------|
## [1] "multicore environment is OK -- using the BiocParallel package"
## [1] "sanity check complete"
## [1] "rab.all  complete"
## [1] "rab.win  complete"
## [1] "rab of samples complete"
## [1] "within sample difference calculated"
## [1] "between group difference calculated"
## [1] "group summaries calculated"
## [1] "effect size calculated"
## [1] "summarizing output"

## [1] "multicore environment is is OK -- using the BiocParallel package"
## [1] "removed rows with sums equal to zero"
## [1] "computing iqlr centering"
## [1] "data format is OK"
## [1] "dirichlet samples complete"
## [1] "clr transformation complete"
## [1] "running tests for each MC instance:"
## |------------(25%)----------(50%)----------(75%)----------|
## [1] "multicore environment is OK -- using the BiocParallel package"
## [1] "sanity check complete"
## [1] "rab.all  complete"
## [1] "rab.win  complete"
## [1] "rab of samples complete"
## [1] "within sample difference calculated"
## [1] "between group difference calculated"
## [1] "group summaries calculated"
## [1] "effect size calculated"
## [1] "summarizing output"

## [1] "multicore environment is is OK -- using the BiocParallel package"
## [1] "removed rows with sums equal to zero"
## [1] "computing iqlr centering"
## [1] "data format is OK"
## [1] "dirichlet samples complete"
## [1] "clr transformation complete"
## [1] "running tests for each MC instance:"
## |------------(25%)----------(50%)----------(75%)----------|
## [1] "multicore environment is OK -- using the BiocParallel package"
## [1] "sanity check complete"
## [1] "rab.all  complete"
## [1] "rab.win  complete"
## [1] "rab of samples complete"
## [1] "within sample difference calculated"
## [1] "between group difference calculated"
## [1] "group summaries calculated"
## [1] "effect size calculated"
## [1] "summarizing output"

ALDEx2plot_Rocks %<>% cbind(., Var1 = "Dolomite", Var2 = "Limestone")
ALDEx2plot_DolSoil %<>% cbind(., Var1 = "Dolomite", Var2 = "Loess soil")
ALDEx2plot_DolDust %<>% cbind(., Var1 = "Dolomite", Var2 = "Dust")
ALDEx2plot_LimeSoil %<>% cbind(., Var1 = "Limestone", Var2 = "Loess soil")
ALDEx2plot_LimeDust %<>% cbind(., Var1 = "Limestone", Var2 = "Dust")

ALDEx2plot_all <- bind_rows(ALDEx2plot_Rocks, ALDEx2plot_DolSoil, ALDEx2plot_DolDust, ALDEx2plot_LimeSoil, ALDEx2plot_LimeDust)
ALDEx2plot_all$Var2 %<>%
    factor() %>%  # Taxa_rank is calcuted for the taxa box plots
    fct_relevel(., "Limestone")

# paste0(percent(sum(ALDEx2plot_Rocks$effect > 0 & ALDEx2plot_Rocks$Significance == "Pass")/nrow(ALDEx2plot_Rocks)), "/", percent(sum(ALDEx2plot_Rocks$effect < 0 & ALDEx2plot_Rocks$Significance == "Pass")/nrow(ALDEx2plot_Rocks)))

Labels <- c(
  paste0("⬆", sum(ALDEx2plot_Rocks$effect > 0 & ALDEx2plot_Rocks$Significance == "Pass"), " ⬇", sum(ALDEx2plot_Rocks$effect < 0 & ALDEx2plot_Rocks$Significance == "Pass"), " (", nrow(ALDEx2plot_Rocks), ")"),
  paste0("⬆", sum(ALDEx2plot_DolSoil$effect > 0 & ALDEx2plot_DolSoil$Significance == "Pass"), " ⬇", sum(ALDEx2plot_DolSoil$effect < 0 & ALDEx2plot_DolSoil$Significance == "Pass"), " (", nrow(ALDEx2plot_DolSoil), ")"),
  paste0("⬆", sum(ALDEx2plot_DolDust$effect > 0 & ALDEx2plot_DolDust$Significance == "Pass"), " ⬇", sum(ALDEx2plot_DolDust$effect < 0 & ALDEx2plot_DolDust$Significance == "Pass"), " (", nrow(ALDEx2plot_DolDust), ")"),
  paste0("⬆", sum(ALDEx2plot_LimeSoil$effect > 0 & ALDEx2plot_LimeSoil$Significance == "Pass"), " ⬇", sum(ALDEx2plot_LimeSoil$effect < 0 & ALDEx2plot_LimeSoil$Significance == "Pass"), " (", nrow(ALDEx2plot_LimeSoil), ")"),
  paste0("⬆", sum(ALDEx2plot_LimeDust$effect > 0 & ALDEx2plot_LimeDust$Significance == "Pass"), " ⬇", sum(ALDEx2plot_LimeDust$effect < 0 & ALDEx2plot_LimeDust$Significance == "Pass"), " (", nrow(ALDEx2plot_LimeDust), ")")
)
Label_text <- bind_cols(
  unique(ALDEx2plot_all[c("Var1", "Var2")]),
  Label = Labels
  )

Other plots

Other plots in the paper which are not based on sequence data ### Isotopes profile

Isotopes <-
  read_csv(
    "Data/Isotopes_data.csv"
  )

Isotopes %<>% 
  mutate(Mean.Arid = (`Limestone Shivta Fm. NWSH1` + `Limestone Shivta Fm. NWSH2`) / 2)
Isotopes %<>% 
  mutate(Mean.Hyperarid = (`Dolomite Gerofit Fm.UVSL5` + `Dolomite Gerofit Fm.UVSL6` ) / 2)

Isotopes2plot <- data.frame(
  Rock = factor(c(rep("Limestone", 10), rep("Dolomite", 10)), 
                levels = c("Limestone", "Dolomite")),
  Depth = rep(Isotopes$`Depth (mm)`, 2),
  Isotope = rep(Isotopes$Isotope, 2),
  min = c(
    pmin(
      Isotopes$`Limestone Shivta Fm. NWSH1`,
      Isotopes$`Limestone Shivta Fm. NWSH2`
    ),
    pmin(
      Isotopes$`Dolomite Gerofit Fm.UVSL5`,
      Isotopes$`Dolomite Gerofit Fm.UVSL6`
    )
  ),
  max = c(
    pmax(
      Isotopes$`Limestone Shivta Fm. NWSH1`,
      Isotopes$`Limestone Shivta Fm. NWSH2`
    ),
    pmax(
      Isotopes$`Dolomite Gerofit Fm.UVSL5`,
      Isotopes$`Dolomite Gerofit Fm.UVSL6`
    )
  ),
  mean = c(Isotopes$Mean.Arid, Isotopes$Mean.Hyperarid)
)

p_isotopes <-
  ggplot(Isotopes2plot, aes(y = mean, x = Depth, colour = Isotope)) +
  geom_point(size = 4, alpha = 1 / 2) +
  geom_errorbar(aes(ymin = min, ymax = max), alpha = 1/2, width = 0.2) +
  geom_line(alpha = 1 / 2) +
  coord_flip() +
  theme_cowplot(font_size = 18, font_family = f_name) +
  background_grid(major = "xy",
                  minor = "none") +
  scale_x_reverse(limits = c(4.1, -0.1), expand = c(0.01, 0.01)) +
  # scale_x_continuous(limits = c(0, 50), expand = c(0.01, 0.01)) +
  facet_grid(Rock ~ . , scales = "free_x", labeller = label_parsed) +
  scale_color_manual(values =  pom4[c(2,1)],
                     labels = c(expression(paste(delta ^ {13}, "C")),
                                expression(paste(delta ^ {18}, "O")))) +
  ylab(expression(paste(delta ^ {13}, "C / ",
                        delta ^ {18}, "O", " (", "\u2030", ")"
  )))

p_isotopes <- plot_grid(p_isotopes, labels = "b", label_size = 20)
print(p_isotopes)

Desiccation experiment

Sample Intercept b a P R2
Dolomite Present 97.2 -0.85 0.01 0.002 0.86
Dolomite Removed 91.9 -2.42 0.03 0.004 0.77
Limestone Present 97.4 -0.99 0.01 0.000 0.96
Limestone Removed 92.5 -3.83 0.05 0.000 0.92
call Model df AIC BIC logLik Test L.Ratio p-value
mod_all lme.formula(fixed = RWC ~ poly(Time, 2, raw = TRUE), data = data2model, random = ~0 + Time | Replicate) 1 5 1108.742 1122.884 -549.3711 NA NA
mod_treatment lme.formula(fixed = RWC ~ poly(Time, 2, raw = TRUE) * BRC, data = data2model, random = ~0 + Time | Replicate) 2 8 1024.040 1046.472 -504.0198 1 vs 2 90.70266 0
call Model df AIC BIC logLik Test L.Ratio p-value
mod_all lme.formula(fixed = RWC ~ poly(Time, 2, raw = TRUE), data = data2model[data2model$BRC == “Present”, ], random = ~0 + Time | Replicate) 1 5 409.0114 419.5658 -199.5057 NA NA
mod_treatment lme.formula(fixed = RWC ~ poly(Time, 2, raw = TRUE) * Rock, data = data2model[data2model$BRC == “Present”, ], random = ~0 + Time | Replicate) 2 8 409.0676 425.5512 -196.5338 1 vs 2 5.943792 0.1143771
call Model df AIC BIC logLik Test L.Ratio p-value
mod_all lme.formula(fixed = RWC ~ poly(Time, 2, raw = TRUE), data = data2model[data2model$BRC == “Removed”, ], random = ~0 + Time | Replicate) 1 5 530.3667 540.9211 -260.1834 NA NA
mod_treatment lme.formula(fixed = RWC ~ poly(Time, 2, raw = TRUE) * Rock, data = data2model[data2model$BRC == “Removed”, ], random = ~0 + Time | Replicate) 2 8 525.9703 542.4538 -254.9851 1 vs 2 10.39642 0.0154802
call Model df AIC BIC logLik Test L.Ratio p-value
mod_all lme.formula(fixed = RWC ~ poly(Time, 2, raw = TRUE), data = data2model[data2model$Rock == “Limestone”, ], random = ~0 + Time | Replicate) 1 5 562.9452 573.4996 -276.4726 NA NA
mod_treatment lme.formula(fixed = RWC ~ poly(Time, 2, raw = TRUE) * BRC, data = data2model[data2model$Rock == “Limestone”, ], random = ~0 + Time | Replicate) 2 8 497.8492 514.3328 -240.9246 1 vs 2 71.09599 0
call Model df AIC BIC logLik Test L.Ratio p-value
mod_all lme.formula(fixed = RWC ~ poly(Time, 2, raw = TRUE), data = data2model[data2model$Rock == “Dolomite”, ], random = ~0 + Time | Replicate) 1 5 555.4483 566.0027 -272.7241 NA NA
mod_treatment lme.formula(fixed = RWC ~ poly(Time, 2, raw = TRUE) * BRC, data = data2model[data2model$Rock == “Dolomite”, ], random = ~0 + Time | Replicate) 2 8 529.4140 545.8975 -256.7070 1 vs 2 32.03428 5e-07

##  setting  value                       
##  version  R version 3.4.4 (2018-03-15)
##  system   x86_64, linux-gnu           
##  ui       X11                         
##  language en_GB                       
##  collate  en_GB.UTF-8                 
##  tz       Europe/Prague               
##  date     2018-07-11                  
## 
##  package              * version   date      
##  abind                  1.4-5     2016-07-21
##  acepack                1.4.1     2016-10-29
##  ade4                   1.7-11    2018-04-05
##  affy                   1.56.0    2018-07-10
##  affyio                 1.48.0    2018-07-10
##  agricolae            * 1.2-8     2017-09-12
##  ALDEx2               * 1.10.0    2018-07-10
##  AlgDesign              1.1-7.3   2014-10-15
##  ape                    5.1       2018-04-04
##  artyfarty            * 0.0.1     2018-07-11
##  assertthat             0.2.0     2017-04-11
##  backports              1.1.2     2017-12-13
##  base                 * 3.4.4     2018-03-16
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##  bayesm                 3.1-0.1   2017-07-21
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##  CRAN (R 3.4.4)                                 
##  Bioconductor                                   
##  CRAN (R 3.4.4)                                 
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##  CRAN (R 3.4.4)                                 
##  CRAN (R 3.4.4)                                 
##  CRAN (R 3.4.4)                                 
##  Github (pmartinezarbizu/pairwiseAdonis@17be405)
##  CRAN (R 3.4.4)                                 
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##  CRAN (R 3.4.4)                                 
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##  CRAN (R 3.4.4)                                 
##  Bioconductor                                   
##  CRAN (R 3.4.4)                                 
##  CRAN (R 3.4.4)                                 
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##  Bioconductor                                   
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##  Bioconductor                                   
##  CRAN (R 3.4.4)

References

Chen, Jun, and Li Chen. 2017. “GMPR: A novel normalization method for microbiome sequencing data.” bioRxiv, February, 112565. https://doi.org/10.1101/112565.

Fernandes, Andrew D., Jean M. Macklaim, Thomas G. Linn, Gregor Reid, and Gregory B. Gloor. n.d. “ANOVA-Like Differential Expression (ALDEx) Analysis for Mixed Population RNA-Seq.” PLOS ONE 8 (7):e67019. Accessed June 7, 2018. https://doi.org/10.1371/journal.pone.0067019.

McMurdie, Paul J., and Susan Holmes. n.d. “Phyloseq: An R Package for Reproducible Interactive Analysis and Graphics of Microbiome Census Data.” PLOS ONE 8 (4):e61217. Accessed July 10, 2018. https://doi.org/10.1371/journal.pone.0061217.